System and method for single-scan rest-stress cardiac pet

ABSTRACT

The present invention provides a system and method for performing a single-scan rest-stress cardiac measurement. In one aspect, the system includes a positron emission tomography (PET) imaging system, a source of a first PET radiotracer for administration to a subject, a source of a second PET radiotracer for administration to a subject, and a processor. The processor has non-transient computer readable media programmed with instructions to obtain PET images of the subject administered with the radiotracer. Furthermore, the computer readable media is programmed with instructions to process the PET images with a non-steady-state, multi-compartment parametric model. An output of the non-steady-state, multi-compartment parametric model is a measure of myocardial blood flow for both a rest state and a stress state of the subject.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on, claims the benefit of, and incorporatesherein by reference U.S. Provisional Application No. 61/704,149, filedSep. 21, 2012, and U.S. Provisional Application No. 61/790,329, filedMar. 15, 2013.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under NIH R56HL095076and R01HL110241 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

BACKGROUND OF THE INVENTION

The disclosure relates, in general, to systems and methods forperforming positron emission tomography (PET) and, more particularly, tosystems and methods for the development and application of parametricmodeling to long half-life tracers, such as ¹⁸F-based tracers formyocardial blood flow (MBF) analysis.

Dynamic measurements with PET are a main stay of research, allowing, forexample, absolute measurement of metabolic rates, transport rates, andreceptor densities; and yet there are few, if any, clinical applicationsthat make full use of the quantitative capabilities of PET. Clinicalapplications continue to be based, primarily, on visual inspection ofstatic images.

With respect to clinical significance, coronary artery disease (CAD)represents an enormous public health burden with a prevalence of 7.6% inthe US in 2009 and 1.3 million new cases each year. Myocardial perfusionalready plays a central role in noninvasive diagnosis, risk assessment,and guiding management decisions of CAD. Specifically, cardiacrest-stress studies have long been used to diagnose and guide treatmentof CAD with single photon emission computed tomography (SPECT) andpositron emission tomography (PET). Most studies have been qualitativein that the interpretation relies on visual comparison of single imagesobtained during rest and stress. A long-standing problem is how totemporally separate the spatial distributions of radionuclide laid downduring rest and stress. Typically, each rest/stress study includes twotracer injections, one for the baseline condition and one during thestress test. To avoid significant commingling of activity from rest andstress studies, one must delay the start of the stress study for 3-5half-lives, a delay that adds logistical complexity and cost whileinconveniencing the patient. Short-lived positron emittingradiopharmaceuticals offer one solution to this problem but requireeither an on-site cyclotron for production of ¹⁵O-, ¹³N-, ¹¹C-compoundsor, alternatively, an ⁸²Rb generator. Therefore, it is desirable todevelop the theoretical basis and a practical measurement strategy thatallows for fully quantitative measurements of myocardial blood flow(MBF) to be obtained during a single 20-30 min rest-stress imagingsession using tracers with long physical half-lives.

This goal is timely because ¹⁸F-based tracers, such as ¹⁸F-Flurpiridazand ¹⁸F-BFPET, are currently under commercial development for cardiacimaging applications. Previously, the validity of measuring MBF using¹⁸F-Flurpiridaz with a two-compartment model has been shown bycomparison with microsphere reference measurements. Phase III clinicaltrials of ¹⁸F-Flurpiridaz are now ongoing.

The introduction of new ¹⁸F-labeled tracers provides an opportunity toextend the range of clinical studies to quantitative mapping offunction, applications that are better suited to the monitoring oftherapy and the staging of disease than are studies based on visualinterpretation of static images. The long physical half-life (109 min)of ¹⁸F-based tracers allows for distribution for clinical studieswithout the necessity for an on-site cyclotron. In addition,contemporary PET cameras have advanced to the point where quantitativemeasurements of myocardial radioactivity concentration can be made withhigh temporal sampling and good statistical precision. Because the heartand great vessels can be included in the field of view, it is possibleto measure simultaneously the concentration history of activity in themyocardium and ventricular cavities, opening the way for absolutequantitation of local myocardial blood flow during rest andpharmacologically induced stress. However, as explained above, the longhalf-life of ¹⁸F also creates a technical challenge for performing botha rest and stress study within a single image session. Furthermore,there are no established and optimized methods suitable for clinicalmeasurement of dynamic function with ¹⁸F-labeled tracers.

Another problem that limits the use of dynamic function measurements inthe clinic is the low signal-to-noise ratio (SNR) of parametric images.Low SNR can be accommodated in research studies because the endpointsare averages measured in cohorts of subject; whereas, clinical studiescompare the measurement of a single subject against a normative cohort.Thus a higher SNR is required for diagnostic purposes or for monitoringparametric change due to therapy.

Although precise in nature, invasive approaches are limited by the risksassociated with coronary artery catheterization. Myocardial perfusionSPECT and PET remain amongst the most widely used non-invasive methodsto evaluate microvascular function in patients with obstructive CAD.However, clinical SPECT and nonquantitative PET are limited in detectingblood flow abnormalities when there is uniform global reduction ofmyocardial blood flow (MBF), as they can only assess relativedifferences. This scenario occurs in patients with left main or triplevessel coronary artery disease, as well as with diffuse endothelialand/or microvascular dysfunction, and yields “balanced ischemia”, i.e.,uniform global reduction in tracer uptake during hyperemic stress. Theyare also limited when monitoring the response to therapy, due to thenecessity of visual analysis rather than absolute quantitation of bloodflow.

From a strictly technical point of view, the field is positioned to makea transition to more quantitative imaging. Contemporary PET cameras arecapable of dynamic measurements. Computational resources are adequate.Among other things, what is lacking is an adequate SNR in parametricimages and a compelling application, one that can achieve wide clinicalutilization.

SUMMARY OF THE INVENTION

The present invention overcomes the aforementioned drawbacks byproviding a system and method for obtaining both rest and stress bloodflow measurements during for example, a single scan session that maylast approximately 20 to 30 minutes. An ¹⁸F-labeled tracer may be usedto perform the PET process.

In a first embodiment, a system is provided for performing a single-scanrest-stress cardiac measurement. The system includes a positron emissiontomography (PET) imaging system, a source of a first PET radiotracer foradministration to a subject, a source of a second PET radiotracer foradministration to a subject, and a processor having non-transientcomputer readable media programmed with instructions to obtain PETimages of the subject administered with the radiotracer. The computerreadable media is further programmed with instructions to process thePET images with a non-steady-state, multi-compartment parametric model.An output of the non-steady-state, multi-compartment parametric model isa measure of myocardial blood flow for both a rest state and a stressstate of the subject.

In one aspect, the PET images are acquired during a single scan sessionon the order of about 20 to about 30 minutes.

In another aspect, at least one of the first and second PET radiotracershas a half-life of at least about 60 minutes. In some embodiments, atleast one of the first and second PET radiotracers is an ¹⁸F-basedtracer. In other embodiments, the ¹⁸F-based tracer is ¹⁸F-Flurpiridaz.

In a second embodiment, a system is provided for performing asingle-scan rest-stress cardiac measurement. The system includes apositron emission tomography (PET) imaging system, a source of a firstPET radiotracer for administration to a subject, a source of a secondPET radiotracer for administration to a subject and a processor havingnon-transient computer readable media. The media is programmed withinstructions to obtain PET listmode data from the subject administeredwith the radiotracer and instructions to process the PET listmode databy way of a direct parametric reconstruction (DPR) of kinetic parametersfrom the listmode data. An output of the DPR is a measure of myocardialblood flow for both a rest state and a stress state of the subject.

In a third embodiment, a system is provided for estimating kineticparameters of blood flow of a subject. The system includes a source of aPET radiotracer for administration to a subject, a positron emissiontomography (PET) imager, and a processor having non-transient computerreadable media programmed with instructions to obtain PET image datafrom the subject. The PET image data is acquired and timed with at leastone real-time measurement of a physiological variance of the subject.The system further includes a model for estimating kinetic parameterswithin of a kinetic region of interest in the PET image data. Thekinetic parameters are based on an expression of the radiotracer withinthe PET image data, and the model is stored in the non-transientcomputer readable media. The product of the model is a report withinnon-transitory computer readable memory of at least one kineticparameter of blood flow based on the kinetic parameters estimated fromthe PET image data.

In one aspect, the kinetic parameters are based on a real-timemeasurement of physiological variance of the subject. In another aspect,the model is capable of executing a direct parametric reconstruction(DPR) of the kinetic region of interest. In yet another aspect, the atleast one kinetic parameter of blood flow comprises myocardial bloodflow during both rest and stress states of the subject . In stillanother aspect, the radiotracer is administered prior to the rest stateand again prior to the stress states of the subject. In someembodiments, the stress state is induced pharmacologically.

In a further aspect, the PET image data is acquired within a total timeperiod of about 30 minutes or less. In another aspect, the at least onereal-time measurement of a physiological variance of the subjectcomprises a computation of ventricle input functions. In someembodiments, the computation of ventricle input functions comprisesgeneralized factor analysis and dynamic orthogonal grouping.

In still other aspects, the radiotracer permits multi-compartmentkinetic modeling and has a high first-pass extraction rate with few orslow biochemical reactions. In some embodiments, the few or slowbiochemical reactions result in long tissue residence periods. In yetother aspects, the multi-compartment kinetic modeling is extended toinclude time varying parameters reflecting the both rest and stressconditions. In one aspect, the radiotracer comprises ¹⁸F-Flurpiridaz. Inanother aspect, the DPR comprises incorporation of time-of-flightinformation pertaining to the PET images.

The foregoing and other aspects and advantages of the invention willappear from the following description. In the description, reference ismade to the accompanying drawings which form a part hereof, and in whichthere is shown by way of illustration a preferred embodiment of theinvention. Such embodiment does not necessarily represent the full scopeof the invention, however, and reference is made therefore to the claimsand herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C are graphic representations showing experimental diagrams ofthe traditional or standard method for PET rest-stress tests (FIG. 1A),a Rust method for PET rest-stress tests (FIG. 1B), and a method inaccordance with the present invention (FIG. 1C), including the injectiontime-line, time-dependence of MBF, blood and tissue time-activity curvesand the compartment models.

FIGS. 2A-2B are graphs showing the sensitivity analysis of K₁ as afunction of study duration and k₃. The error bars represent the SDestimated from the covariance matrix. FIG. 2A shows K₁ as a function ofstudy duration. FIG. 2B shows K₁ as a function of k₃.

FIG. 3 is a bar graph showing the estimated MBF when dipyridamole isused as the stressor. The second tracer injection dose is equal to thefirst tracer dose. On the x-axis, the number 1-3 denote the simulationperformed based on different representative subjects.

FIG. 4 is a bar graph that shows plots of the average bias calculatedfrom the MBF estimated with MGH1 and MGH2 models using the MBF from thestress-only as reference. The mean bias is computed by pooling the sameregion over the subjects.

FIG. 5 is a bar graph that shows plots of the average standard deviationcalculated from the MBF estimated with MGH1 and MGH2 models using theMBF from the stress-only as reference. The mean standard deviation iscomputed by pooling the same region over the subjects.

FIG. 6A provides a plurality of images and FIG. 6B is a polar map thatgraphically shows representative rest/stress ¹⁸F-Flurpiridaz PET studyresults. FIG. 6A shows the 10-min frame reconstructed imagecorresponding to 15-25 min of the rest and stress studies as a fusedimage over the three axes (short axis, vertical and horizontal longaxes). FIG. 6B shows the polar map of the estimated coronary/cardiacflow reserve (CFR) for this subject.

FIG. 7 is a schematic diagram of an emission tomography system inaccordance with the present invention.

FIG. 8 is a flow chart setting forth the steps of an example of a methodof using an emission tomography system in accordance with the presentinvention.

FIG. 9 is a graphic illustration of a proposed study protocol inaccordance with the present invention and showing transition of thekinetic parameters and the time activity curves in LV and myocardium.Due to the effect of dipyridamole, MBF (K₁(t)) undergoes transitionalchanges before reaching the peak flow (K₁(t) therefore is a variablewith time). Kinetic parameters of K₁˜k₄ can be estimated by fitting thewhole time-activity curves to the model. In one aspect, there is no needto separate the activity residual of the rest study from the stressstudy. T_(s) and T_(e) denote the start and end of vasodilator infusion.Peak stress is reached at T_(p), while MBF return to normal at Tb.

FIG. 10A is a screen capture of a user-friendly software developed forautomated estimation of input functions using GFADS, as well as kineticmodeling of ⁸²Rb, ¹³N-ammonia, ¹⁵O water and ¹⁸F-Fluripiridaz.

FIG. 10B is a graph that shows factors and factor images estimated withGFADS (note the good separation of LV, RV and myocardium). Datapresented here were acquired with ¹⁸F-Flurpiridaz in a volunteer.

FIGS. 11A-11B are graphs that show a comparison of the RV and LV inputfunctions derived with GFADS and ROIs from simulation studies (FIG. 11A)and a monkey study (FIG. 11B). Compared to either the true inputfunction in simulation or the blood samples in animal data, the GFADSinput function has less partial volume effect and less contaminationfrom spillover.

FIG. 12A is a scatter plot and FIG. 12B show data from a clinicalcardiac study with ¹⁸F-Flurpiridaz. The TAC in FIG. 12A shows thekinetics of the tracer uptake in healthy and ischemic myocardium under aconcurrent rest-stress study. FIG. 12B shows co-registered rest andstress images of the ¹⁸F-Flurpiridaz in clinical cardiac studies. Anischemic defect is present in the inferior-lateral wall of themyocardium.

FIG. 13 is a set of images that show a short-axis and long-axis view ofsummed images from the monkey myocardium.

FIGS. 14A-14C are a series of graphs that show data from a monkey study.FIG. 14A shows the myocardial TAC and the model fit. FIG. 14B is a plotof estimated K₁ as a function of study duration up to 10 min. Standarddeviation is plotted as error bars. FIG. 14C is a plot of estimated K₁as a function of fixed k_(3.)

FIGS. 15A-15B are bar graphs that show a comparison of the estimatedpeak MBF with different models compared to the truth and the MBF fromsequential rest-stress studies by simulation with (FIG. 15A) double 18Fdose for stress and (FIG. 15B) single 18F dose for stress. Theconventional model is based on the reported method (Rust, T. C., et al.,Phys. Med. Biol., 2006. 51: p. 5347-5362.).

FIG. 16 is a graph that shows a correlation plot in synthetic clinicaldata between estimated MBF in concurrent and sequential rest-stressshowing high correlation (r>0.9) and low bias (<3%).

DETAILED DESCRIPTION OF THE INVENTION

The disclosure relates in general to a system and methods for performingsingle-scan, rest-stress cardiac PET and, more particularly, to a systemand method for the development and application of parametric modeling tolong half-life tracers, such as ¹⁸F-based tracers for MBF analysis.

In a first aspect of the present invention, a specific, non-steadystate, two compartment model was developed for rest-stress cardiac PETimaging, while a second aspect of the present invention relates tofurther advanced modeling approaches. With respect to these two aspectsof the present invention, the non-steady state modeling approach will bediscussed first in order to lay the ground work for the more generaldiscussion the will encompass the advanced modeling approach related tothe second aspect of the present invention.

The present system and method is presented in several varyingembodiments in the following description with reference to the Figures,in which like numbers represent the same or similar elements. Referencethroughout this specification to “one embodiment,” “an embodiment,” orsimilar language means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present invention. Thus, appearancesof the phrases “in one embodiment,” “in an embodiment,” and similarlanguage throughout this specification may, but do not necessarily, allrefer to the same embodiment.

The described features, structures, or characteristics of the inventionmay be combined in any suitable manner in one or more embodiments. Inthe following description, numerous specific details are recited toprovide a thorough understanding of embodiments of the system. Oneskilled in the relevant art will recognize, however, that the system andmethod may both be practiced without one or more of the specificdetails, or with other methods, components, materials, and so forth. Inother instances, well-known structures, materials, or operations are notshown or described in detail to avoid obscuring aspects of theinvention.

Any schematic flow chart diagrams included are generally set forth aslogical flow-chart diagrams. As such, the depicted order and labeledsteps are indicative of one embodiment of the presented method. Othersteps and methods may be conceived that are equivalent in function,logic, or effect to one or more steps, or portions thereof, of theillustrated method.

Additionally, the format and symbols employed in any such flow chartdiagrams are provided to explain the logical steps of the method and areunderstood not to limit the scope of the method. Although various arrowtypes and line types may be employed in the flow-chart diagrams, theyare understood not to limit the scope of the corresponding method.Indeed, some arrows or other connectors may be used to indicate only thelogical flow of the method. For instance, an arrow may indicate awaiting or monitoring period of unspecified duration between enumeratedsteps of the depicted method. Additionally, the order in which aparticular method occurs may or may not strictly adhere to the order ofthe corresponding steps shown.

Non-Steady State Modeling Approach

In the present application, a system and method are provided forquantifying tracer kinetics, such as 18F-based tracer kinetics, using aPET imaging system. In one embodiment, the system and methods can beapplied for quantification of myocardial blood flow (MBF) in a humanpatient with both rest and stress studies performed in a single scansession having a duration of about 30 minutes. In order to overcome theaforementioned drawbacks, the feasibility of absolute quantification ofthe MBF within a short, single imaging session was examined by way ofkinetic modeling approaches and simulation.

By way of an overview, three procedures for measuring rest/stressmyocardial blood flow were analyzed and compared. The procedures are:(1) the Standard method, (2) the Rust method, and (3) the MGH method. Inthe Standard method, rest and stress measurements are separated in timeso that residual radioactivity from the rest studies decays tonegligible levels. The Rust method is an adaptation of a previouslydeveloped method (Rust, T. C., et al., Phys. Med. Biol. 51, 5347-5362,2006) to analyze MBF measured with ¹³N-ammonia, with short decay periodbetween rest and stress. Finally, the MGH method is a protocol that hasbeen newly developed. These procedures feature different experimentalprotocols, kinetic assumptions, and analytic methods.

The basic methodological approach was to compare the three methods byMonte Carlo simulation. These simulations are based on a digital phantomwith anthropomorphic cardio-pulmonary geometry. The simulations produceensembles of dynamic measurements that realistically predict both thefour-dimensional evolution of ¹⁸F-concentrations and the statisticalnoise propagation for a variety of cardiac abnormalities in differentmyocardial segments and with different flow responses. The simulationsare designed to compare the noise-to-signal ratio of flow measurementsfor the Standard method versus the MGH method of combined rest-stress.The effect of non-steady-state response to vasodilation was included inthe simulations to investigate the effects on estimation bias. Theensembles of simulated studies were analyzed according to the Standardand MGH methods described above, to determine the mean and standarddeviation of MBF.

Prior to describing the results of Monte Carlo simulations, the basicoptimization of the MGH method with ¹⁸F-Flurpiridaz was considered. Thefirst question addressed is the effect of shortening the observationtime: short observation time ensures that the curves depend primarily onflow, but with the trade off of higher noise levels; whereas protocolswith longer measurement times may yield better signal-to-noise ratio butmust account for the reaction of Flurpiridaz with the mitochondrialcomplex I. Next, the effect of imperfect knowledge of k₃ on theestimation of K₁ was studied. Finally, the noise propagation and bias ink₂ was studied for the case where the measurement period is shortcompared to ln(2)/k₂.

The combined rest-stress measurement protocol is described withreference to FIG. 1. Referring first to FIG. 1A, a conceptual diagram ofthe Standard method is shown in which the rest study and stress studywere performed on separate occasions to ensure that no residual activityfrom the rest study appeared during the stress measurement. The firstpanel in FIG. 1A shows the time-line of the separated rest and stressstudies, with arrows indicating the injection times. At least tenhalf-lives between the two studies were allowed for tracer decay. Thesecond panel shows that K₁ has different constant values in rest(K_(1,r)) and stress (K_(1,s)). The third panel sketches the expectedconcentration histories for the input function and the tissue curvealong with their relation to important fiducial markers, such as startof infusion and the moment of peak stress. For the purpose of thestudies, estimation of MBF from the Standard method was assumed to beunbiased. The Standard protocol was also expected to yield the lowestnoise estimates of MBF at any fixed radiation dose. As discussed above,the price is logistical complexity, higher cost per rest-stress exam,difference in bed positions and the inconvenience of two separate scansessions. The Standard method does not account for non-steady-state flowduring the stress measurement. Therefore, it may yield a biased estimateof flow during stress imaging.

The conceptual design for the rest-stress imaging protocol developed forrest-stress imaging by Rust et al. is included in FIG. 1B. The firstpanel shows the time-line of a single-scan rest-stress protocol, withthe arrows indicating time of injection. The second panel shows theassumed time dependence of K₁. During rest and stress, Rust et al.assume steady state, with different constant values for rest and stressMBF. The third panel sketches the expected shape of the input functionsduring rest and stress. The fourth panel sketches the compartment modelused to describe the tissue activity. Their approach allows the stressstudy to begin before the activity from the rest study decays away. Asshown in FIG. 1B, they assume that the activity from the first injectionevolves throughout the entire experimental period unaffected by thepharmacological perturbation. The sketch also shows that, during thestress measurement period, it is the sum of the residual activity fromthe first injection and the new activity from the second injection,which is measured by the PET camera. The input function from the firstinjection, C_(P,1), must be known throughout the entire rest-stressmeasurement period but only the sum of the two injections is measuredduring the stress period. Extrapolation and subtraction are used toestimate separate input functions. The PET curve is modeled as the sumof rest and stress, using two separate two-tissue models, as detailedherein.

FIG. 1C shows the conceptual design for the newly developed MGHprotocol. The same injection protocol as in the Rust method is used: therest phase, with the first tracer injection followed about 7 to 10 minlater by a vasodilator infusion and then the second tracer injection andstress imaging period. The first panel in FIG. 1C shows that during therest state K₁ is constant, but as vasodilator is infused at time T_(s)blood flow starts to increase, reaches a maximum at peak stress (timeT_(p)), and the peak hyperemia ends at time TE. The flow is expected toreturn to the baseline at T_(B). In the MGH protocol list modeacquisition starts with the first injection and continues, withoutinterruption, throughout the stress period. At peak stress, the secondtracer injection is made. As the whole study duration can be less than30 min, the residual activity from the first tracer injection is mixedwith the activity from the second tracer injection in the period of thestress scan. As shown in the second panel of FIG. 1C, the residualtracer concentration is affected by the infusion of vasodilator evenbefore the second tracer injection as a result of the pharmacologicalaction of the vasodilator. The effect of the vasodilator continues toaffect the residual activity throughout the rest of the study. Due tothe vasodilator, the subject is now in a non-steady-state with respectto blood flow; that is blood flow may be changing during themeasurement. A basic assumption underlying conventional kineticmodeling, constant blood-to-tissue transport rates and fluxes, is nolonger valid. Therefore, compartment modeling was extended to allowflow-related parameters to vary with time in response to the action ofvasodilators.

Kinetic models were used to analyze the three protocols as shown inFIGS. 1A-1C. While similar kinetic models were used, there are severalnotable differences in the detailed assumptions. The fourth panel ofFIG. 1A depicts the kinetic model underlying the Standard model, whichis designed to analyze rest and stress imaging as independent events.The fluxes and rate constants are considered to be fixed at constantvalues. The compartment diagram for rest imaging is the same for theRust and MGH models as these two models differ only in how stressimaging is analyzed. The fourth panel in FIG. 1B shows the compartmentdiagram for stress imaging in the Rust model. The Rust model predictsthe PET curve will be the sum of activity from the rest and stressinjections. Rust and colleagues assume that the two tracer injectionsare independent and superimpose a rest and a stress study, each withconstant flow, to predict/simulate the observed composite PET tissuecurve. The Rust model does not explicitly model the effect ofpharmacological challenge on the first injection. As a result, the Rustmodel assumes that residual activity from the rest phase of the study isunaffected by the changes wrought by vasodilator challenge. The stateequations for the Rust model can be expressed as

$\begin{matrix}{{\frac{C_{F,1}}{t} = {{K_{1,r}C_{P,1}} - {\left( {k_{2,r} - k_{3}} \right)C_{F,1}} + {k_{4}C_{B,1}}}}{\frac{C_{B,1}}{t} = {{k_{3}C_{F,1}} - {k_{4}C_{B,1}}}}{\frac{C_{F,2}}{t} = {{K_{1,s}C_{P,2}} - {\left( {k_{2,s} + k_{3}} \right)C_{F,2}} + {k_{4}C_{B,2}}}}{\frac{C_{B,2}}{t} = {{k_{3}C_{F,2}} - {k_{4}C_{B,2}}}}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

where the subscript “1” and “2” denote the activity concentration fromthe first and second injection, respectively.

In the case of the MGH method, it was first consider how a hypothetical¹⁸F-tracer gains access to an elemental tissue volume. It was assumedthat the tracer is not metabolized in plasma and is free or ininstantaneous equilibrium with plasma proteins and possibly othercellular and subcellular constituents. The molar concentration of thetracer in plasma is denoted as C_(p)(t), irrespective of the number oftracer injections. The inward flux is K₁(t)·C_(p)(t), where K₁(t)=F(t)·E is the product of the local myocardial blood flow and theunidirectional extraction fraction, E. For simplicity, E was assumed tobe a constant, independent of time. The time-varying clearance rate oftracer from tissue to blood is described by k₂(t). The tissue space isconceptualized as two compartments, one representing free tracer,C_(F)(t), the other compartment representing reacted tracer molecules,C_(B)(t). Only the free tracer molecules in blood and tissue can respondto changes in blood flow; increases in flow will affect K₁(t) and k₂(t),resulting in observable changes in the kinetic curves measured by thePET camera. The reacted molecules were considered to be unaffected byflow changes and they cause the long tracer residence time in tissue.For generality, the reaction was left unspecified. But, for example, itcan represent a receptor binding reaction or the committed step in ametabolic process. For short measurement times, the amount of tracerreturning from the reacted pool can be assumed to be negligible.

Analysis of the MGH protocol uses the two tissue-compartment kineticmodel, to allow for tracers with high first pass extraction and longtissue residence time. In the MGH protocol, the two-tissue compartmentmodel is extended to include important characteristics of a rest-stressstudy, as shown in the bottom panel of FIG. 1C: First, the twoinjections of tracers are treated as a single entity—the input functionC_(p)(t). The ventricular concentration history is measured during theentire study. During the transition from rest to stress there is achange in physiological state. Residual tracer molecules from the reststudy can generally not be distinguished from those injected later.Residual activity from the rest study is observed in combination withthe radioactivity injected during pharmacologic stress. Second, stressimaging is reconsidered to account for the changes due to the use ofvasodilating drugs. In the past, it has been assumed, either implicitlyor explicitly, that blood flow changes abruptly from one constant valuein the rest condition to a different constant value in the stresscondition. However, previous reports have shown that the myocardialblood flow is altered by slow intravenous infusion of vasodilating drugsin a progressive way over 2 to 7 min, depending on the drug. One way todeal with this phenomenon is to assume that K₁ and k₂ can be representedby smooth functions of time that can be described by a small number ofextra parameters. In the simplest case, K₁ and k₂ could merely undergo astep change but in a more realistic approximation, K₁ and k₂ areconstant during the rest phase and then have a unimodal shape as aresult of stress.

Second, the compartment representation was extended for rest-stressapplications. As in conventional compartment modeling, the concept thatthe concentration in a compartment is spatially homogeneous and temporalchanges occur simultaneously throughout the compartment was retained. Aninnovation in this development is that the flow-related transport ratesare allowed to change as a function of time. Further, the probabilityper unit time for transport (k₃) into the reacted compartment wasassumed to be unaffected by vasodilating drugs. Accordingly, the basicdifferential equations can be formulated as

$\begin{matrix}{{\frac{{C_{F}(t)}}{t} = {{{K_{1}(t)}{C_{P}(t)}} - {\left\lbrack {{k_{2}(t)} + k_{3}} \right\rbrack {C_{F}(t)}}}}{\frac{{C_{B}(t)}}{t} = {k_{3}C_{F}}}} & \left( {{Eq}.\mspace{14mu} 2} \right)\end{matrix}$

where K₁(t) and k₂(t) are functions of time dependent on thepharmaceutical properties of vasodilators. During the rest phase of thestudy, K₁ and k₂ are assumed to be constant. Following the infusion ofvasodilator, K₁(t) and k₂(t) are represented by time-varying functions,specified with parameters estimated from the PET data. Equation 2 has aclosed-form solution given by

$\begin{matrix}{{{C_{F}(t)} + {C_{B}(t)}} = {{^{- {\int_{0}^{t}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}}\bullet {\int_{0}^{t}{\left\lbrack {^{\int_{0}^{u}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}\ {K_{1}(u)}{C_{P}(u)}} \right\rbrack {u}}}} + {k_{3}{\int_{0}^{t}{\left\{ {^{- {\int_{0}^{s}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}}\ \bullet {\int_{0}^{s}{\left\lbrack {^{\int_{0}^{u}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}\ {K_{1}(u)}{C_{P}(u)}} \right\rbrack {u}}}} \right\} {s}}}}}} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

Two functional forms were considered, namely, MGH1 and MGH2, forparameters K₁(t) and k₂(t). For the MGH1 model, K₁ and k₂ are altered asa smooth function of vasodilator kinetics, infusion rate, and duration.Two drugs, adenosine and dipyridamole, have been commonly used inclinical studies. It has been reported that during the intravenousinfusion of adenosine and dipyridamole, there is a transitional phase offlow increase until peak hyperemia is reached for both vasodilators butthe functional form of the transition is unclear. For simplicity it isapproximated as a linear function in this work, as shown in the firstpanel of FIG. 1C. Similarly, once the blood flow begins to decline frompeak hyperemia, the transitional change is assumed to be a linearfunction of time until the baseline flow is reached. K₁(t) is expressedas

$\begin{matrix}{{K_{1}(t)} = {{K_{1,r}{u\left( {T_{S} - t} \right)}} + {\frac{{K_{1,r}\left( {T_{P} - T_{S}} \right)} + {\left( {K_{1,s} - K_{1,r}} \right)\left( {t - T_{S}} \right)}}{\left( {T_{P} - T_{S}} \right)} \times {u\left( {T_{P} - t} \right)}{u\left( {t - T_{S}} \right)}} + {K_{1,s}{u\left( {t - T_{P}} \right)}{u\left( {T_{E} - t} \right)}} + {\frac{{K_{1,r}\left( {T_{E} - T_{B}} \right)} + {\left( {K_{1,s} - K_{1,r}} \right)\left( {t - T_{B}} \right)}}{\left( {T_{E} - T_{B}} \right)} \times {u\left( {t - T_{E}} \right)}{u\left( {T_{B} - t} \right)}} + {K_{1,r}{u\left( {t - T_{B\;}} \right)}}}} & \left( {{Eq}.\mspace{14mu} 4} \right)\end{matrix}$

where u(t) is the unit step function. In Eq. (4), four time points areused to describe the transitions: (1) beginning of infusion (T_(S)), (2)peak hyperemia (T_(P)), (3) end of peak hyperemia (T_(E)), and (4)baseline flow (T_(B)). Note that the peak hyperemia may not end rightafter the end of vasodilator infusion, especially for dipyridamole. Thebaseline and peak MBF is defined as K_(1,r) and K_(1,s). The same rampfunction is also used to describe the time course of k₂ with k_(2,r) andk_(2,s) as k₂ at the baseline condition and peak hyperemia,

$\begin{matrix}{{k_{2}(t)} = {{k_{2,r}{u\left( {T_{S} - t} \right)}} + {\frac{{k_{2,r}\left( {T_{P} - T_{S}} \right)} + {\left( {k_{2,s} - k_{1,r}} \right)\left( {t - T_{S}} \right)}}{\left( {T_{P} - T_{S}} \right)} \times {u\left( {T_{P} - t} \right)}{u\left( {t - T_{S}} \right)}} + {k_{2,s}{u\left( {t - T_{P}} \right)}{u\left( {T_{E} - t} \right)}} + {\frac{{k_{2,r}\left( {T_{E} - T_{B}} \right)} + {\left( {k_{2,s} - k_{2,r}} \right)\left( {t - T_{B}} \right)}}{\left( {T_{E} - T_{B}} \right)} \times {u\left( {t - T_{E}} \right)}{u\left( {T_{B} - t} \right)}} + {k_{2,r}{u\left( {t - T_{B\;}} \right)}}}} & \left( {{Eq}.\mspace{14mu} 5} \right)\end{matrix}$

Although Eq. (5) is realistic in terms of the physiology, using it toestimate the MBF and CFR (cardiac flow reserve) requires prior knowledgeor the estimation of T_(P), T_(E), and T_(B) (T_(S) is fixed and known).In practice, such timing information can be acquired with externalmonitoring of the blood pressure or by analyzing the ECG signals. Tosimplify the model and eliminate the need to measure these three timepoints, a simplified model MGH2 was also formulated by removing thetransition of the parameters and assuming the parameters to change theirvalues by an immediate switch at the time of vasodilator infusion, asthe time-dependence of K₁ and k₂ described by

$\begin{matrix}{{K_{1}(t)} = \left\{ {{\begin{matrix}{K_{1,r},} & {t < T_{S}} \\{K_{1,s},} & {t \geq T_{S}}\end{matrix}{k_{2}(t)}} = \left\{ \begin{matrix}{k_{2,r},} & {t < T_{S}} \\{k_{2,s},} & {t \geq T_{S}}\end{matrix} \right.} \right.} & \left( {{Eq}.\mspace{14mu} 6} \right)\end{matrix}$

Following development, the kinetic models were applied to the observedmeasurements. Regardless of the kinetic model chosen or the detailedphysiologic assumptions, it is desirable to account for the transientlyhigh blood radioactivity concentrations and finite resolution effectsthat are included in the PET measurements. Some myocardial radioactivityrecorded by the PET camera is not due to radioactivity in myocardialtissue. We model this effect and estimate its magnitude as part of thefitting process, according to Eq. (3):

$\begin{matrix}{{{PET}(t)} \approx {{f_{lv} \cdot {C_{lv}(t)}} + {f_{rv} \cdot {C_{rv}(t)}} + {^{- {\int_{0}^{t}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}}\bullet {\int_{0}^{t}{\left\lbrack {^{\int_{0}^{u}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}\ {K_{1}(u)}{C_{P}(u)}} \right\rbrack {u}}}} + {k_{3}{\int_{0}^{t}{^{- {\int_{0}^{u}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}}\  \times {\int_{0}^{\mu}{{K_{1}(u)}{C_{P}(u)}^{\int_{0}^{u}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}\ {{u} \cdot {\mu}}}}}}}}} & \left( {{Eq}.\mspace{14mu} 7} \right)\end{matrix}$

where f_(lv) and f_(rv) represent the spillover fractions for left andright ventricles, respectively.

Referring now to the rest of the Figures, the first result deals withestimating blood flow at rest. As expected on theoretical grounds, thepart of the PET curve most highly dependent on blood flow was shown tobe the initial period after IV injection of ¹⁸F-tracers, such as¹⁸F-Flurpiridaz. FIG. 2A shows a graph of K₁ values versus measurementtime for the rest state. The measurement duration varies in 1 minincrements, from 1 to 10 min. For short measurements, less than 3 minduration, there is a positive bias. Increasing the measurement period,reduces the statistical error in estimating K₁, though gains insignal-to-noise ratio diminish, becoming minimal beyond a 5 minmeasurement period. The SD of estimated K₁ was 2% with 7 min of data and1.8% with 10 min of data. FIG. 2B shows the variation in estimated K₁ asa function of the nominal value of k₃. The result clearly shows that K₁is nearly the same (ranged from 0.498 to 0.507), for k₃ values between 0and 0.1 min⁻¹.

The clearance rate for tracer from tissue to blood is represented by k₂.Simulations demonstrated that a longer measurement period is needed toachieve stable k₂ values and lower noise levels in the estimates. With 7to 10 min of data, the SD for k₂ was 10.4% and 6.2%. A strong positivecorrelation between k₃ and k₂ was also found where fixing k₃ at highervalues increases the least squares estimate of k₂.

Bar graphs are presented in FIG. 3 summarizing the range of simulatedabnormal responses to dipyridamole stress. The injected doses for FIG. 3for the two tracer injections were identical. The different stressmodels/protocols include: (1) simulations of stress only as the Standardmethod, (2) simulations of MGH1, the piecewise linear ramp, and (3)simulations of MGH2, with step-change in flow between rest and stress.Estimates from the MGH1 were found to be close to the estimates from theStandard method in all combinations. MGH2, on the other hand, shows agreater bias in some simulations, usually modest underestimation of MBF.

The comparison of bias and variance of MBF estimation from each model ispresented in FIGS. 4 and 5. FIG. 4 shows the bias averaged over thesubjects for each region. MGH1 is found to be nearly unbiased, with biasbetween −1% and 3%. No significant pattern of bias dependency on themyocardial sector was observed. In general, a higher tracer dose for thestress study reduces the bias for MGH1 method. On the other hand, MGH2has a higher bias than the MGH1 method. For dipyridamole, anunderestimation of MBF is found in all regions with bias ranging between−1% and −10%. Higher bias is observed in the single-dose stress studies.When adenosine is used as the vasodilator, the bias of MGH2 is fairlylow, ranging between −2% and 2%. The average SD of the estimated MBF isshown in FIG. 5. The stress-only protocol, as the Standard method, hasthe lowest noise estimate, less than 5% in SD, for all the segments andconditions simulated. MBF estimates from MGH1 and MGH2 are of highervariance, ranging between 2% and 9%. Variance of the MGH1 and MGH2estimates is reduced to less than 6% when double tracer dose is used forthe stress study. In general, MGH2 has a lower SD than the MGH1 method.

Finally, FIG. 6 shows a representative myocardial rest and stress studywith the short axis, vertical and horizontal long axes with rest in grayscale and stress in color along with the corresponding coronary flowreserve (ratio of absolute myocardial blood flow at rest and during peakstress).

Considering first the “Standard” method, most contemporary quantitativemethods assume that measurement of resting MBF can be described as asteady state condition and analyzed by compartment models with constanttransport rates and rate constants. This is similar to the approachtaken by Nekolla et al. who validated ¹⁸F-Flurpiridaz in animals bycomparison with microsphere measurements. The analysis used in thepresent system and methods deviates from the conventional Standardmethod in that blood flow and flow-related rate constants are allowed tobe time dependent in response to pharmacological stress. Based on priorresearch, different time-variation can be expected, depending on thedetailed properties of the vasodilator. Previous work has not consideredthis effect and it should be taken into account, as not doing so may addto the bias and variability of measurements. The present system andmethods parameterize a mathematically convenient functional form forF(t) so that estimating these parameters provides a summary descriptionof the temporal flow variation. As a practical matter, adopting a morerealistic mathematical function may lead to unidentifiable estimates ofMBF. So, a carefully thought out trade off is required.

Another point to consider is the validation of flow measurements madeduring stress imaging. Microsphere flow estimates are considered to bethe gold standard but the derivation of the microsphere flow equationsalso assumes constant flow. The assumption of constant flow enters themicrosphere method through the Fick equation:

∫₀ ^(T) F(t)C _(a)(t)dt≡F∫ ₀ ^(T) C _(a)(t)dt   (Eq. 8)

where F is the myocardial blood flow and C_(a)(t) is the arterialconcentration history of microspheres. This means that the accuracy ofthe microsphere method during stress imaging depends on the magnitude ofthe systematic error made by factoring the Fick equation.

There are a number of approaches to combined rest-stress imaging thathave been considered. Among those are what can be calledsemiquantitative or ad hoc methods that use larger doses of radionuclidefor stress imaging and neglect the effect of prior rest imagingentirely. “Subtraction” of rest images from stress images can improvecontrast but it is not based on rigorous physiological and physicalprinciples. More quantitative methods have been proposed in variousreports. Kim et al. proposed (Kim, K. M., et al., J. Nucl. Med. 51,1624-1631, 2010) and lida et al. implemented (lida, H., et al., J. Nucl.Med. 51, 1624-1631, 2010) a related model and measurement protocol formeasuring the change in cerebral blood flow elicited with pharmacologicchallenge. The Rust model is most relevant to imaging with ¹⁸F-labledMBF tracers (Rust, T. C., et al., Phys. Med. Biol. 51, 5347-5362, 2006).The Rust kinetic model predicts that the activity measured during thestress exam is the sum of the residual rest activity plus the newactivity injected during pharmacological stress. Using the kineticparameters estimated from fitting the rest images, the Rust modelpredicts the residual contributions to the stress images. Rust et al.assume that MBF is constant during stress imaging but also mention that“kinetic parameters would not be constant due to the infusion ofadenosine during the scan” as a limitation of their model.

With respect to the present system and methods, the feasibility ofabsolute quantification of rest/stress MBF within a single, shortimaging session was re-examined. Unlike previous methods, free tracermolecules were assumed to “feel” the effects of stress as a change instate whether they be part of residual activity from a previousinjection, or “newer” molecules injected during infusion of apharmacological stressor. In effect, once pharmacologic stress isstarted, the system is assumed to have no memory of the previous state.This key insight allows for treatment of the entire rest-stressmeasurement as a single study with a single input function that includesthe contributions from both the rest and stress tracer injections.Transport and rate constants describing the inflow and egress ofunreacted tracer molecules become time dependent and the equations nolonger describe a classical compartment system.

Since, a priori, the functional form of the time dependence is notknown, it is necessary to assume a convenient functional form for thefluxes and rate constants. Both a piecewise linear (MGH1 method) and asimpler two state formulation (MGH2 method) are described and evaluatedby the simulation. Despite the more general and realistic assumptionsthe new method is not significantly more complicated than moreconventional analyses.

In one example, a long rest study obtained with ¹⁸F-Flurpiridaz wasanalyzed. The first few minutes of the dynamic sequence were shown to beinsensitive to the mechanisms that account for the long residence timeof Flurpiridaz. In terms of compartment modeling, FIG. 2 shows thatestimates of MBF reach a high precision, requiring only a few minutes ofdata, while being insensitive to physiologically plausible values of k₃.This means that binding of Flurpiridaz can be treated as a nominaleffect which does not vary from subject to subject. From FIG. 2, it canbe concluded that estimates of MBF with low bias and good statisticalprecision can be obtained in less than 10 min. Therefore, it isgenerally not necessary to prolong the rest study and pharmacologicalstress infusion can begin promptly, allowing for a shorter study design.

While the finding that only a few minutes are needed to obtainquantitative measurements of resting flow is of basic importance, thereare other important questions to answer about combined rest/stress.These questions have to do with (1) whether it is possible to obtainaccurate and precise MBF with a rapid single- scan rest/stress protocolsunder plausible physiologic assumptions, and (2) what the trade-off isbetween accuracy and signal-to-noise ratio due to overlapping rest andstress studies. Considering the Standard method with completely separaterest and stress measurement to be method of lowest radiation dose,another question relates to the level of activity required to obtain asignal-to-noise ratio similar to the MGH protocols. In one aspect, thesequestions can be answered by realistic Monte Carlo simulations based ona digital torso phantom with an anthropomorphic cardiac geometry. Byfollowing millions of photon histories, an ensemble of simulated dynamicstudies was constructed with both normal and abnormal myocardialsegments. Ensembles of data emulating the Standard method and the MGH1method were simulated. The myocardial time-activity curves fromsimulated data were fitted to the Standard method, the MGH1 and MGH2methods. The following properties of these three methods were found byevaluating the bias and precision performance. First, the Standardmethod produced the most precise estimates for the peak MBF duringstress. This is because, in the Standard method, rest and stress studiesare performed separately, so there is no mixture of the activity fromthe two tracer injections. This results in a simple and straightforwardparameter estimation strategy, which provides the best precision.Second, combining a rest and stress study results in an increase in theparameter variance. For both MGH1 and MGH2, the activity after the onsetof vasodilator infusion is affected by the time-dependence of K₁ and k₂.Furthermore, the model becomes more complicated than the Standard methodand since the measured activity comes from two injections, the parametervariance will be increased for both MGH1 and MGH2. Therefore, choosingbetween the Standard method and the MGH methods becomes a trade-offbetween the study complexity, injected dose and the parameter precision.Considering all these factors, we believe that the convenience of asingle scan session will outweigh the minor increase in the variance ofMBF.

Based on the current simulations, the MGH method can be a useful toolfor rest/stress studies. By examining the performance of the MGH1method, the MBF bias was found to be small (<3%). As previouslymentioned, the MGH1 method has a higher variance than the Standardmethod, but this increase of variance is modest as shown in FIG. 5. Thestandard deviation of MBF is less than 5% for double-dose and less than9% for single-dose stress. The Standard method requires that the subjectbe repositioned for the rest study; whereas the MGH method offers ashort, single-scan session protocol.

There are two variants of the MGH method. In one aspect, the MGH1 methodis more accurate due to the more detailed model of the effects ofvasodilators. However, the MGH2 method, as a simplified version of MGH1,also produced reasonable performance. By modeling the transition of MBFas a step change, fewer parameters need to be estimated for MGH2. As aresult, MGH2 can be more precise than MGH1. However, since theapproximation of the step change does not fully describe the kineticbehavior of MBF, MGH2 can be more biased than the MGH1 method. This isparticularly obvious in the case as dipyridamole, as it takes longer(6.5 min) to reach the peak hyperemia than does adenosine (1.5 min). Inother words, the MBF change in the MGH2 model can better approximate thefast transition in adenosine than dipyridamole. As a result, the MGH2method can be more appropriate for fast-acting vasodilators as theaccuracy of the MGH2 method is more dependent on the functional form ofthe time-dependent kinetics.

Compared to the Rust method, the MGH method is different in severalimportant features: First, residual tracer molecules in plasma andmyocardium are transported according to flow conditions which prevailduring stress. Second, flow and clearance was modeled as a continuousfunction during stress imaging to account for the effect of thevasodilator. The modeling is the same whether the tracer molecule wasintroduced at rest or during vasodilation. Because the residual activityof the rest study is more properly modeled, a lower bias was expected.Third, the input function of the tracer is treated as a single entity inthe MGH method while the Rust method requires the input functions fromthe two injections to be separated and extrapolated. In the currentsystem and methods, a judgment on the performance of the Rust method wasbit made as the simulated data are based on the K₁ and k₂ kineticsdescribed by the MGH1 model. The MGH1 model is an approximation and canbe further validated with animal or human data. The bias/precisionperformance of the Rust and MGH methods can benefit from furthervalidation with real experimental data against reference MBF measuredwith microspheres.

Although this work is aimed toward application of ¹⁸F-Flurpiridaz, themodeling approach is general and can be applied to other tracers withrelatively long half-lives such as ¹³N-ammonia. The model may also beapplied for vasodilators other than the adenosine and dipyridamole oreven exercise stress studies that can be performed with a patient underthe fixed bed position within the scanner.

The system and methods described, in one embodiment, contemplate the useof an in vivo assay to measure MBF. While specific examples areprovided, it is contemplated that a general implementation will includethe use of an suitable PET tracer molecule that is compatible with oneor more of a variety of medical imaging technologies. For example, inone embodiment, a method includes the steps of administering the tracer¹⁸F-Flurpiridaz for PET imaging. However, the tracer molecule may not beused as a positron emitter, and instead may be designed to emit adifferent type of radiation or even display no radioactivity at all.Similarly, the tracer molecule may not be used for PET imaging, butrather be compatible with magnetic resonance imaging (MRI), ultrasoundimaging, or any other type of suitable imaging technology.

Referring to FIG. 7, a PET system 100 in accordance with the presentinvention includes an imaging hardware system 110 that includes adetector ring assembly 112 about a central axis, or bore 114. Anoperator work station 116 including a commercially-available processorrunning a commercially-available operating system communicates through acommunications link 118 with a gantry controller 120 to controloperation of the imaging hardware system 110.

The detector ring assembly 112 is formed of a multitude of radiationdetector unit 122 that produces a signal responsive to detection of aphoton on communications line 124 when an event occurs. A set ofacquisition circuits 126 receive the signals and produce signalsindicating the event coordinates (x, y) and the total energy associatedwith the photons that caused the event. These signals are sent through acable 128 to an event locator circuit 130. Each acquisition circuit 126also produces an event detection pulse that indicates the exact momentthe interaction took place. Other systems utilize sophisticated digitalelectronics that can also obtain this information regarding the preciseinstant in which the event occurred from the same signals used to obtainenergy and event coordinates.

The event locator circuits 130 in some implementations, form part of adata acquisition processing system 132 that periodically samples thesignals produced by the acquisition circuits 126. The data acquisitionprocessing system 132 includes a general controller 134 that controlscommunications on a backplane bus 136 and on the general communicationsnetwork 118. The event locator circuits 130 assemble the informationregarding each valid event into a set of numbers that indicate preciselywhen the event took place and the position in which the event wasdetected. This event data packet is conveyed to a coincidence detector138 that is also part of the data acquisition processing system 132.

The coincidence detector 138 accepts the event data packets from theevent locator circuit 130 and determines if any two of them are incoincidence. Coincidence is determined by a number of factors. First,the time markers in each event data packet must be within apredetermined time window, for example, 0.5 nanoseconds or even down topicoseconds. Second, the locations indicated by the two event datapackets must lie on a straight line that passes through the field ofview in the scanner bore 114. Events that cannot be paired are discardedfrom consideration by the coincidence detector 138, but coincident eventpairs are located and recorded as a coincidence data packet. Thecoincidence data packets are provided to a sorter 140. The function ofthe sorter in many traditional PET imaging systems is to receive thecoincidence data packets and generate memory addresses from thecoincidence data packets for the efficient storage of the coincidencedata. In that context, the set of all projection rays that point in thesame direction (θ) and pass through the scanner's field of view (FOV) isa complete projection, or “view”. The distance (R) between a particularprojection ray and the center of the FOV locates that projection raywithin the FOV. The sorter 40 counts all of the events that occur on agiven projection ray (R, θ) during the scan by sorting out thecoincidence data packets that indicate an event at the two detectorslying on this projection ray. The coincidence counts are organized, forexample, as a set of two-dimensional arrays, one for each axial imageplane, and each having as one of its dimensions the projection angle θand the other dimension the distance R. This θ by R map of the measuredevents is call a histogram or, more commonly, a sinogram array. It isthese sinograms that are processed to reconstruct images that indicatethe number of events that took place at each image pixel location duringthe scan. The sorter 140 counts all events occurring along eachprojection ray (R, θ) and organizes them into an image data array.

The sorter 140 provides image datasets to an imageprocessing/reconstruction system 142, for example, by way of acommunications link 144 to be stored in an image array 146. The imagearrays 146 hold the respective datasets for access by an image processor148 that reconstructs images. The image processing/reconstruction system142 may communicate with and/or be integrated with the work station 116or other remote work stations.

Referring to FIG. 8, a flow chart is provided that sets forth the stepsof an example of a method 200 of using an emission tomography system inaccordance with the present invention. In a first step 202 of the method200, the subject is imaged using an emission tomography system such as aPET system. The subject is imaged for a period of time based on thekinetics of the at least two tracer doses to be administered. Forexample, when the tracer is ¹⁸F-Flurpiridaz, the subject can be imagedof the course of about 20 to about 30 minutes for the calculation ofMBF. However, if it is desirable to calculate other physiologicalresults, then the subject can be imaged for a longer overall time frameor beginning at a later time beginning with or at a later point afterthe step of administering a first dose.

In a next step 204 of method 200, a first dose of a tracer molecule,such as an ¹⁸F-based tracer molecule, is administered. One example of an¹⁸F-based tracer is the PET tracer ¹⁸F-Flurpiridaz. The dose is, in oneaspect, administered to a subject such as a human patient. During step204, imaging of the subject continues, for example, to measure traceractivity associated with a rest state of the subject.

In a next step 206 of the method 200, a vasodilator is administered tothe subject. In one aspect, the purpose of the vasodilator is totransition the state of the subject to a stress state. Accordingly, thevasodilator may be substituted for another compound or means of inducinga stress state in the subject. As in step 204, imaging of the subjectcontinues in step 206, for example, to measure tracer activityassociated with a transition from a rest state to s stress state of thesubject.

In a next step 208 of method 200, a second dose of a tracer molecule,such as an ¹⁸F-based tracer molecule, is administered. In some aspects,step 208 can be similar to step 204. However, it is possible that theamount of the dose and/or the type of the tracer administered can vary.For example, a larger dose of the same tracer administered in step 204can be administered in step 208. During step 208, imaging of the subjectcontinues, for example, to measure tracer activity associated with astress state of the subject.

In a next step 210 of method 200, the one or more images of the subjectacquired with the emission tomography system can be stored in anon-transitory computer readable media. Then, in a step 212 of method200, the one or more images can be analyzed, for example, with acomputer processor. The analysis step can include the application of akinetic model to the acquired image data. One example of a suitablekinetic model is a non-steady state, two compartment model as describedherein. Following analysis, in step 214, one or more calculations can bemade. The calculations can include quantification of subject perfusionand MC-I expression levels. Furthermore, in optional step 216 of method200, the subject myocardium can be diagnosed as hibernating myocardium,healthy myocardium and/or ischemic myocardium.

With respect to steps 210 and 212, the images acquired with the emissiontomography system and the program for application of the kinetic modelcan be stored in non-transitory computer readable media associated withone or more computer processing systems. For example, PET data can beacquired with and stored on one computer system, while the PET data canbe analyzed with the kinetic model a separate computer system. The oneor more computer systems can further be linked by way a wired orwireless connection for the transfer of data between the one ore morecomputer systems.

With reference to the above method 200, it is possible that implementthe steps in the order shown or in any order suitable to the successfulexecution of the method 200. For example, imaging of the subject in step202 may be carried out prior to and concurrent with administration ofthe doses in any of steps 204, 206 and 208. In another example, the step212 of analysis can be intermittent with imaging over the course ofmethod 200. While a number of examples have been described, other usefulcombinations of the steps of method 200 are both likely and anticipated.

Advanced Modeling Approach

While in a first aspect of the present invention, a specific, non-steadystate, two compartment model was developed for rest-stress cardiac PETimaging, a second aspect of the present invention relates to furtheradvanced modeling approaches. In one aspect, important innovations formeasurement of myocardial blood flow are provided including: (i) directparametric reconstruction (DPR) of MBF kinetic parameters from listmodeprojection data, and (ii) a strategy for estimating both rest and stressmyocardial blood flow in a single scan session (concurrent rest-stress).

The present methodology reduces the variance of kinetic parameterestimates by estimation directly from listmode projection data. Thereduction of parameter variance can include more accurate estimates ofthe noise (Poisson) available for the projection data than are availablein the image domain, and temporal regularization provided within the DPRprocess via the kinetic model. This method acts on listmode rather thansinogram data, preserving the high temporal resolution of the raw PETdata, and includes quantitative corrections for attenuation, detectorsensitivity, scatter and random coincidences so as to yield un-biasedestimates of the parametric maps. In general, DPR benefits from a priorstandard reconstruction of the concentration data. The standardreconstruction may be used to estimate the input function, to define avalid kinetic region of interest definition (KRI) of regions suitablefor analysis, and/or to provide information about the kinetics ofregions that are not suitable for analysis with the kinetic mode. Directparametric reconstruction (DPR) is then performed only in the KRI ratherthan in the whole image, which allows for a reduction in the computationtime while generally reducing or eliminating the need to fit pixelsoutside the KRI. To keep the projection operation un-biased, it ispossible to use the observed TACs of pixels lying outside the kineticregion of interest (KRI), which are known from the initialreconstruction step, and model the time-course of pixels located insidethe KRI using the kinetic model. One advantage of this technique isthat, by reducing noise in the estimation of the parametric map, itallows a broader range of trade offs between the injected dose ofradioactivity and the precision of the parametric map. The presentdirect reconstruction, listmode framework also allows incorporation ofadditional information (such as the energy of individual photons andtime-of-flight information) in the estimation process, a feature thatcan further improve the quantitative accuracy of kinetic parameterestimation. It has been demonstrated that a significant reduction of atleast 3-6 fold in bias and variance of kinetic parameters estimates canbe achieved with our proposed methods (Bias±variance improved from85±181% to 11±57%). Furthermore, it has been shown that varianceassociated with the presently provided direct estimation of kineticparameters approach was generally unaffected when dose was reduced from4 to 0.3 mCi unlike traditional kinetic estimation methods fromreconstructed frames.

Parametric images of cardiac function were estimated for concurrentrest-stress studies that preclude the need for separate rest and stressimaging sessions while reducing artifacts related to differences inpatient positioning and condition as well as attenuation paths ondifferent imaging visits. In one example, the patient is injected atrest, and again 10-15 min later during pharmacologic stress before thefirst injection has washed out/decayed. Previous methods attempting toachieve rest-stress within a short delay have been based on theincorrect approximation that a concurrent scheme is simply a summationof sequential rest-stress studies. The provided concurrent rest-stresskinetic model, on the other hand, has been shown to describe the traceruptake in a comprehensive and rigorous way that allows the flowparameters to be time-dependent and results in equations with a simpleinterpretation. In one aspect, accurate simultaneous estimation of restand stress kinetic parameters in concurrent rest-stress was confirmed.No statistically significant difference between the kinetic parametersestimated during concurrent and sequential rest-stress was observed.

In certain embodiments, the provided system an methods includes severalcomponents. A first component analyzes the mechanisms that affecttransport of an ¹⁸F-flow tracer such as ¹⁸F-Flurpiridaz, and devises anovel strategy for sequential as well as simultaneous measurement ofrest-stress parametric flow maps in a single ˜30 min. scan session. Inone aspect, the first component can enable a fully automatedquantitation of MBF based on reconstructed concentration data usingnon-invasive computation of the left and right ventricle input functionsusing generalized factor analysis (GFADS) and dynamic orthogonalgrouping (DOG) to increase SNR (expected factor 2-3) in the parametricimages without compromising quantitative accuracy.

A second component can include direct parametric reconstruction (DPR) ofthe listmode data stream to compute the rest-stress flow maps withsignificantly lower parameter variance. In some aspects, the componentcan enable the ability to include the routine corrections forattenuation, scatter and random coincidences and b) the ability tosegment the reconstruction volume into different regions, those in whichthe kinetic model is valid and others where the concentration historieswill be used without modeling. In some examples, DPR

Another embodiment of the present system and methods relates to thedevelopment and evaluation of two novel approaches for estimation ofkinetic parameters. In one aspect, an automated kinetic modelingapproach is developed to estimate flow parameters from dynamicreconstructed PET. In one aspect, a strategy is provided to quantifylocal MBF using partially trapped tracers such as ¹⁸F-Flurpiridaz. Inone example, the strategy uses IV injection and PET listmodeacquisition. The listmode data are integrated into time epochs (frames)such that the early frames are acquired at higher temporal frequency,when the concentrations are changing rapidly; whereas, later frames areintegrated for a longer period, when the concentrations are changingmore slowly. Presently, parametric cardiac imaging is performed onreconstructed dynamic PET frames using voxel-by-voxel (or ROI) kineticanalysis. This requires manual definition of the cardiac blood pool asleft and right ventricular ROIs and produces moderately accurateparametric flow maps, with poor SNR. In contrast, the provided approachhas been demonstrated to be fully automated, with non-invasiveestimation of the right and left ventricle input functions, as well asaccurate estimation of the kinetic parameters from reconstructed volumesor listmode projection data yielding accurate results that are robust tonoise and very reproducible.

In another aspect, a flow measurement strategy incorporates knowledge ofthe basic properties of partially trapped tracers with a two-tissuecompartment model, allowing simplifications appropriate to quantitativeflow measurements. The full kinetic model is shown in FIG. 9. In thismodel the tracer can be in one of two states, either free, as theinjected molecule, or bound to a receptor. In the example case of¹⁸F-Flurpiridaz, previous studies have shown that it binds to themitochondrial complex I. The product of plasma concentration (C_(p)) andtransport rate K₁ is the force driving tracer from capillary plasma tothe free space. Symbolically, K₁ is the plasmato-tissue transport rate,FE, the product of flow F and the unidirectional capillary extractionfraction, E. k₂ is a rate constant summarizing the egress from tissue toplasma. The rate constants k₃ and k₄ provide a summary description ofbinding and release of tracer. The full operational equation for the twocompartment model is well known. In one aspect a focus is directed tothe first few minutes after IV injection. First, it has beendemonstrated that K₁ is an applicable approximation to blood flow sincethe unidirectional extraction fraction is greater than 90% at allphysiological flow rates. Second, the ratio k_(3/)(k₂+k₃), is between0.2-0.6, meaning that after the first few minutes of scanning, abouthalf the tracer entering the tissue space is bound and the effect ofbinding must be considered when interpreting the data. Third, it hasbeen shown that at least an hour of scanning is required to obtain goodestimates of all parameters. Fourth, the data have shown that byrestricting the scan period to about 5 minutes duration, the modelanalysis can be simplified and blood flow (K₁) estimated with goodaccuracy and higher SNR. Conversely, the ability can be lost to measurethe other parameters.

In one example, a simplified approach, suitable for analyzing the first5-10 minutes of scanning, either a sequential rest or stressmeasurement, is to fix k₃ to a nominal value and set k₄=0 (i.e. toassume the release of bound tracer is negligible during the scanperiod). The simplified operational equation is Eq. 9:

$\begin{matrix}{{C_{T}(t)} \approx {{\left( {\phi_{lv} + \frac{V_{wb}}{V_{t}}} \right){C_{lv}(t)}} + {\phi_{rv}{C_{rv}(t)}} + {K_{1}\bullet \; {{C_{P}(t)} \otimes ^{{- {({k_{2} + k_{3}})}}t}}} + {K_{1}\bullet \; k_{3}{\int_{0}^{t}{^{- {({k_{2} + k_{3}})}^{u}}{{u} \cdot {\mu}}}}}}} & \left( {{Eq}.\mspace{14mu} 9} \right)\end{matrix}$

where C_(T)(t) is the instantaneous tissue concentration, φ is thespillover fraction of left or right ventricle (subscript lv or rv),V_(wb)/V_(T) is tissue blood volume, C_(lv)(t) and C_(rv)(t) are theconcentration histories for the left and right ventricle, respectively,K₁ is the flow extraction product, C_(p)(t) is the plasma concentrationhistory. Referring to FIGS. 14A-14C, the data show that for the first 10minutes, the flow estimate is insensitive to the values of k₃ and k₄.There are four parameters to estimate in the operational equation:

$\begin{matrix}{\left( {\phi_{lv} + \frac{V_{wb}}{V_{t}}} \right),} & \left. 1 \right) \\{\phi_{rv},} & \left. 2 \right) \\{K_{1},} & \left. 3 \right) \\{k_{2}.} & \left. 4 \right)\end{matrix}$

Results obtained in realistic Monte Carlo cardiac simulations andcynomolgus monkey studies show that K₁ can be estimated with low biasand high precision with 5 minutes of scanning.

Generalized Factor Analysis of Dynamic sequences (GFADS) is a powerfulapproach that allows for extraction of temporal data (i.e., timeactivity curves), and the corresponding spatial distributions (i.e.,factor images), from a series dynamic images (13. El Fakhri, G., et al.,J Nucl Med, 2000. 41: p. 1400-1408; El Fakhri, G., et al., Med Phys,2006. 33: p. 1016-1024). This approach has many advantages overROI-based approaches that include automation, robustness to noise(estimation of TAC from all voxels instead of an ROI) andreproducibility. A dynamic sequence of N PET frames can be representedby an M×N matrix A, where M is the number of voxels in a dynamic PETstudy. The observed data is described as a linear combination of factorsF, where the coefficients of the combination are defined in C:

$\begin{matrix}{A_{m\; i} = {{\sum\limits_{p = 1}^{P}\; {C_{m\; p}F_{pi}}} + n}} & \left( {{Eq}.\mspace{14mu} 10} \right)\end{matrix}$

F contains P factors and n denotes noise in the data, yielding (N−P)Mdegrees of freedom in the model. The factor curves, F, define the timecourse of the factors whose spatial definition is contained in matrix C(factor image). In order to solve (Eq. 10), the number of factors P mustbe known a priori. Estimation of the factors F and factor images C isbased on minimization of:

$\begin{matrix}{{f_{PLS}\left( {C,F} \right)} = {{\sum\limits_{m = 1}^{M}\; {\sum\limits_{i = 1}^{N}\; \left( {A_{m\; i} - {\sum\limits_{p = 1}^{P}\; {C_{m\; p}F_{pi}}}} \right)^{2}}} + {a\left\lbrack {{\sum\limits_{m = 1}^{M}\; {\sum\limits_{p = 1}^{P}\; {H\left( C_{m\; p} \right)}}} + {\sum\limits_{p = 1}^{P}\; {\sum\limits_{i = 1}^{N}\; {H\left( F_{pi} \right)}}}} \right\rbrack}}} & \left( {{Eq}.\mspace{14mu} 11} \right)\end{matrix}$

where H(x) is equal to 0 for and equal to x≧0 for x² for x<0, and α is apenalty coefficient that can be chosen from a wide range of values. Thereproducibility and accuracy of the approach has been validated in morethan 1000 clinical ⁸²Rb and ¹³N-ammonia studies as well as in an acuteischemic canine model and microspheres (El Fakhri, G., et al., J NuclMed, 2009. 50: p. 1062-1071; El Fakhri G., et al., Circulation. 2007,116 (16): 717).

In another aspect, the system and methods can include a dynamicorthogonal grouping (DOG) approach. The estimation of kinetic parameterson a voxel-by-voxel basis yields very noisy parametric images due tohigh levels of noise in TAC derived from single voxels. Using afiltering approach reduces noise but results in a degradation of spatialresolution of the parametric images. In order to reduce noise withoutcompromising spatial resolution, a DOG approach can be applied. The DOGapproach has been applied to ⁸²Rb-82 [Kimura, Y., et al., Phys. Med.Biol., 1992. 47: p. 455-468) to perform 3D grouping of voxels withsimilar time course into several groups of size G (that we callmacro-voxels). DOG consists of two steps: Step 1: An orthogonal analysisis performed in every dynamic voxel TAC_(i)(t). The reduced-dimensionaldata subspace S that contains most of the data is a 3D subspace,consistent with the number of GFADS factors. The coordinates of eachdynamic voxel in S are calculated as the product of the principalvectors with TAC_(i)(t), i.e., 3D vectors P(TAC_(i)(t))_(τ) are obtainedwhere τ=1 . . . 3 describes the location of a given dynamic voxelTAC_(i)(t) in the 3D data sub-space S, and P denotes the projectionoperator onto the τ^(th) base vector. Step 2: The grouping algorithm isapplied to the 3D vectors P(TAC_(i)(t))_(τ). Euclidean distances arecalculated between each two dynamic voxels and the dynamic voxel forwhich the sum of distances to all other voxels is highest is determined.Next, the G-1 closest dynamic voxels are found. These G dynamic voxelsconstitute the first group. The procedure is repeated until all dynamicvoxels are grouped (El Fakhri, G., et al., I J Nucl Med, 2005. 46: p.1264-1271). Finally, the estimated kinetic parameters are remapped ontoa kinetic parametric volume.

Yet another aspect of the present system and methods includesdevelopment of a strategy for direct estimation of kinetic parametersfrom listmode projections. In conventional parametric imaging, kineticparameters are estimated by fitting pixel-wise TACs which typicallyresults in very noisy parametric maps. Direct reconstruction of kineticparameters from projection data, (i.e., parametric reconstruction), canreduce noise in parametric images or, for a given noise level, reducethe injected dose. The noise reduction is due to 1) the fact that noiseis better modeled in the projection domain (Poisson) than in the imagedomain (approximately Gaussian but difficult to estimate and 2) the useof temporal regularization within the reconstruction process provided bythe kinetic model. Most existing DPR approaches require linearization ofthe kinetic model (e.g., using the Patlak method) and are therefore notgeneral. Others have proposed methods for the direct estimation of oneand two-compartment model kinetic parameters; however these have onlybeen tested in simple scenarios on simulated data not accounting forattenuation, variable detector sensitivities and random and scattercoincidences. In one aspect of the present system and methods, fourdifferent methods were implemented, each having different computationalrequirements. A pixel and sinogram-wise DPR approach can be developedbased on the EM (sinogram-wise), CG (sinogram-wise), SPR (pixel-wise)and PICD (pixel-wise) algorithms that are applicable in the clinic,(i.e. that are computationally feasible and account for attenuation,detector sensitivity, scatter and random effects). These DPR techniquescan be adapted to listmode data since this format preserves the hightemporal resolution of the dynamic raw data, reduces memory requirementsand enables the use of TOF information in the DPR process.

Another aspect of the present system and methods includes listmode baseddirect parametric reconstruction (DPR). The log-likelihood function ofdynamic listmode data is:

$\begin{matrix}{l = {{\sum\limits_{n = 1}^{N}\mspace{11mu} {\log\left\lbrack {{\sum\limits_{i = 1}^{M}\; {a_{ni}{F\left( t_{n} \middle| \theta_{i} \right)}}} + {{Sc}_{n}\left( t_{n} \right)} + {R_{n}\left( t_{n} \right)}} \right\rbrack}} - {\sum\limits_{i = 1}^{M}\; {s_{i}{\int_{0}^{T}{{F\left( t \middle| \theta_{i} \right)}\ {t}}}}}}} & \left( {{Eq}.\mspace{14mu} 12} \right)\end{matrix}$

where n is the coincidence index, N is the total number of coincidences,i is the image voxel index, M is the total number of voxels in theparametric image, θ_(i) is the vector of kinetic parameters for pixel i,t_(o) is the time of arrival of coincidence n , F(t|θ) is the PETactivity concentration for pixel i calculated using the kinetic modelwith vector of parameters θ, α_(ni) is the primary system matrix elementfor coincidence n and image pixel i (containing attenuation andsensitivity correction factors), s_(i) is the detection sensitivity ofpixel i, T is the duration of the acquisition and Sc_(n)(t) and R_(n)(t)are the average number of scatter and random counts detected in timeframe t and in the line of response (LOR) X_(n). The gradient of Eq. 12with respect to the parametric map θ_(i) is readily computed and dependson the current estimate of the parametric map and the derivative of thekinetic model F.

With respect to spatial regularization, the maximization of Eq. 12 withrespect to the parametric maps is an ill-posed inverse problem.Regularization can be performed by adding a roughness penalty to Eq. 12,acting either on the parametric maps or the image TACs. The presentapproach is regularized in the image domain because 1) this allowscomparison of its performance to that of the indirect approach (i.e.,regularized image reconstruction+pixel-by-pixel TAC fitting) byregularizing it with the same roughness penalty and 2) this introducesonly one regularization strength instead of N (where N is the number ofparametric maps to be reconstructed).

With respect to initialization and input function, Eq. 12 is anon-convex function of the parametric maps, so that it is beneficial tostart the estimation process using a judicious initial guess of theparametric maps, to ensure convergence toward its global maximum. Thepresent system and methods use the standard reconstruction of theconcentration data and GFADS to estimate the LV and RV concentrationhistories and the myocardial factor image to determine the KRI regionfor DPR. Since Eq. 9 is of the form:C(t,φ_(lv),φ_(rv),K₁,k₂)=φ_(lv)·C_(lv)(t)+φ_(rv)·C_(rv)(t)+K₁·q(t,k₂)where the φ_(rv), φ_(lv), and K₁ enter as linear parameters and q is aknown function. A good initial guess can be formed with the followingprocedure: 1) Tabulate q(t,k₂) for a mesh of k₂ values. 2) For each k₂in the table, find the least squares solution for the linear parameters.3) Choose the solution for which the sum of squares is a minimum. In oneexample, median filtering is used to reduce noise of the estimates ofthe parameters.

To address DPR within a KRI, instead of maximizing Eq. 12 with respectto all pixel kinetic parameters, the present approach maximizes it, inone aspect, only with respect to pixels lying within the mask of themyocardium (this mask may be obtained by masking the myocardium imagefactor obtained with GFADS) and that are known to follow the kineticmodel F. This strategy will, in some aspects: 1) reduce computation timeas it reduces the number of parameters to estimate from a few millionsto a few thousands and 2) reduces the bias in the estimation of thekinetic parameters in the myocardium by avoiding fitting the liver,lungs, bones, etc. TACs with the model F , which they might not follow.To correctly compute projections in the maximization of Eq. 12, thedynamic volume within the DPR process is estimated using reconstructedTACs for pixels lying outside of the volume of interest (VOI) (obtainedin the initial estimation of the parametric maps) and using the kineticmodel for pixels lying in the KRI. This strategy corresponds toreplacing the projection operation in Eq. 12 byΣ_(i∈VOI)α_(ni)F(t_(n)|θ_(i))+Σ_(i∉VOI)α_(ni)ρ_(i)(t_(n)), whereρ_(i)(t_(n)) is the reconstructed image at time t_(n).

Considering quantitative corrections, to obtain low-bias parametricmaps, it is prudent to incorporate estimates of the scatter and randomcoincidences in the projector of Eq. 12 (attenuation and detectorsensitivity correction can be performed as usual in the system matrix).Note that although listmode DPR is frameless, in some aspects, it isnecessary to define frames for the implementation of the scatter andrandom corrections since these are performed using estimated sinogramsof the random and scatter coincidences. This step uses the same set offrames and the same sinograms of random and scatter coincidences asthose used in the initial image reconstruction process in order to avoidestimating them twice. The random sinogram is estimated for every timeframe using the singles approach since this technique is low-bias andvery low-noise. The scatter sinogram is computed using the singlescatter simulation (SSS) and is scaled to the data using tails fitting(TF). Instead of applying SSS frame-by-frame however, the presentapproach can enable application to a reduced number of frames followedby linear interpolation to estimate the scatter distribution in everytime frame. The optimal number of scatter frames needed to yieldlow-bias estimation of the parametric maps can be determined as anaspect of the present system and methods.

Referring now to the choice of the maximization algorithm, severalreconstruction algorithms can be used to maximize Eq. 12. Iterativeschemes for maximizing Eq. 12 involving different DPR algorithms havebeen previously described. Example algorithms include relativeperformances of the conjugate gradient (CG, sinogram-wise), parametriciterative coordinate descent (PICD, pixel-wise) and surrogate parametricreconstruction (SPR, pixel-wise). Each of these algorithms can include aregularization term and quantitative corrections for attenuation,detector sensitivity, scatter and random coincidences and can be appliedin a KRI rather than for all pixels as long as the TACs of pixels lyingoutside of the KMR are known. In order to reduce computation, one aspectof the present system and methods includes precomputing the systemmatrix used in these algorithms (instead of computing it on the fly asis often done in listmode reconstruction). One approach includes the useof pre-computed system matrix reconstruction strategies that have beendeveloped for sinogram data to the reconstruction of listmode data.

Yet another aspect of the present system and methods includes objectiveassessment of the performance of kinetic modeling approaches in CADrelated tasks. In one example, the performance of the methods describedherein can be evaluated in simulated sequential rest and stress studiesgenerated using realistic Monte Carlo simulations modeling the geometryof the patient and the PET scanner as well as scatter and randomcoincidences (Guerin, B. et al., IEEE Nucl Sci, 2008. 55(3): p.942-952). Dynamic studies can be simulated by generating listmode filescorresponding to dynamic structure (i.e., myocardium, myocardialdefects, LV and RV, lungs, bones etc . . . ), generating randomcoincidences detection times according to the TACs of these dynamicstructures and then combining the listmode files corresponding to eachstructure. The TACs used in these simulations can be obtained frompatients imaged with tracers such as ¹⁸F- Flurpiridaz compound.

The performance of the present system and methods can be assessed indynamic physical phantom acquisitions obtained by imaging the differentcompartments of a Data Spectrum cardiac phantom separately. Virtualdynamic cardiac studies can then be obtained by generating randomarrival times of the coincidence in the listmode files corresponding toevery compartment according to the same ¹⁸F-Flurpiridaz patient TACsthan the ones used in the aforementioned Monte Carlo simulations.

Example methods, such as GFADS+DOG and DPR, can be evaluated against thestandard approach of reconstructing a dynamic volume and then fittingthe reconstructed TACs pixel-by-pixel. To ensure that this comparison isfair, all three approaches can be regularized using the same roughnesspenalty acting on TACs (rather than on parametric maps which could alsobe possible in DPR) and can be corrected for attenuation, detectorsensitivity, scatter and random coincidences using the same correctionfactors. Each method can be assessed in terms of variance and bias ofthe parametric maps since the reference maps will be known in theseMonte Carlo simulations and phantom acquisitions.

Next, the performance of the present methods can be assessed in activityestimation tasks related to CAD and compared to pixel-wise TAC fitting.In one aspect, activity distributions can be grouped based on severityand extent of modeled (simulated or acquired) ischemia to generate threeconditions: normal, ischemic, scarred myocardium. Kinetic parameters canbe then estimated in the myocardial defect for each “subject” and binaryclassification into groups performed by calculation of the likelihood ofbelonging to each group. Pairwise linear discrimination tasks can thenbe performed using, for example, estimation ROC analysis. Area under theROC curve (A_(z)) will be used as the performance metric, and resultscompared to a standard.

Another embodiment of the present system and methods relates toextension and evaluation of the kinetic modeling methods to concurrentrest-stress PET. In a first aspect, simultaneous joint estimation ofrest and stress kinetic parameters are developed from reconstructions.FIG. 9. illustrates the central concept that rest-stress measurement isconsidered to be a single study in which the blood flow is timedependent. Previous analytic approaches incorrectly attempted to correctthe concentrations measured during stress by subtracting orextrapolating activity from the (first) rest injection (Rust, T. C., etal., Phys. Med. Biol., 2006. 51: p. 5347-5362). In one aspect,considering the rest-stress measurement to be a single study with twotracer injections and one composite input function allows for rigorousaccounting of the residual “background” of tracer due to the firstinjection. Whatever residual activity remains from the first injectionis naturally accounted for in the kinetic model.

In one example of the present system, the total rest-stress scan periodis on the order of 20 to 30 minutes. Because of the short duration, insome embodiments, simplifying assumptions can be made regarding thekinetics of tracers such as ¹⁸F-Flurpiridaz. The assumptions can includethat 1) the rate constants k₃, k₄ for ¹⁸F-Flurpiridaz and MC-I do notchange over the experimental period; and 2) the release of bound traceris very slow, allowing the return of bound tracer to the free pool to beneglected. The subject remains in the same position throughout thestudy. In some embodiments, there are at least two tracer injections,one when the subject is in the rest state and the other following aninfusion of a vasodilator, such as dipyridamole.

In one example, during the first (rest) phase of the study the transportrates and fluxes are assumed to be constant; with the start of thevasodilator infusion, K₁ and k₂ are considered to be time dependent. Thesecond tracer injection is made at the end of infusion, the timeconsidered to be “peak” stress, when flow is expected to be maximal.Considering these assumptions, Eq. 9 can be modified to accommodate thetime dependence, as in Eq. 13.

$\begin{matrix}{{C_{T}(t)} \approx {{\left( {\phi_{lv} + \frac{V_{wb}}{V_{t}}} \right){C_{lv}(t)}} + {\phi_{rv}{C_{rv}(t)}} + {^{- {\int_{0}^{t}{{({{k_{2}{(z)}} + k_{3}})}{z}}}}\bullet \mspace{11mu} {\int_{0}^{t}{^{\int_{0}^{u}{{({{k_{2}{(z)}} + k_{3}})}\ {z}}}\ {K_{1}(u)}{C_{P}(u)}{u}}}} + {k_{3}{\int_{0}^{t}{^{- {\int_{0}^{u}{{({{k_{2}{(s)}} + k_{3}})}\ {s}}}}\ {\int_{0}^{\mu}{{K_{1}(u)}{C_{P}(u)}^{\int_{0}^{u}{{({{k_{2}{(s)}} + k_{3}})}\ {s}}}\ {{u} \cdot {\mu}}}}}}}}} & \left( {{Eq}.\mspace{14mu} 13} \right)\end{matrix}$

The two tracer injections are considered to yield a single inputfunction. In one embodiment of the present methods, the four-dimensionalPET data are analyzed with GFADS, yielding estimates of the left andright ventricular concentration history for the entire experimentalperiod. In addition to estimating the parameters

$\left( {\phi_{lv} + \frac{V_{wb}}{V_{t}}} \right),$

φ_(rv), k₃, some time dependence for K₁(t) and k₂(t) is assumed. Severaldifferent functional forms can be tested, such as 1) the basichypothesis of instantaneous switch from rest flow to peak flow; and 2) aramp change from rest to peak flow. There are also a number of detailsabout the parameter estimation procedure: a stepwise estimation processcan be useful. First, the rest parameters are estimated using weightednonlinear least squares, assuming a nominal value for k₃. Theseestimates are used in a penalized least squares fit of the entire timecourse, including direct estimation of k₃ and peak stress flow.

The two models are then compared to simpler kinetic models such as theone previously proposed by Rust et al. These models (noted conventionalmodel) assume that TAC from the concurrent rest/stress study is asummation of two independent studies. They do not account for the factthat the kinetic parameters are no longer the same during thepharmacological stress and can lead to significant bias of estimatedMBF.

A further aspect of the present system and methods includes a directestimate of rest and stress kinetic parameters from listmode data. TheDPR method can be extended to the joint estimation of the stress andrest parametric maps in a single imaging session. The expected value ofthe concentration symbolized by F(t,θ) is replaced by operationalequation Eq. 13. In one example, a step-wise joint estimation approachis used that parallels the conventional fitting process: First, restdata is used to estimate the maps of spillover coefficients for RV andLV as well as the K₁ and k₂ maps. Then the entire rest-stress period isfit with Eq. 13 while penalizing the spillover fractions and resting K₁,k₂.

Strategies exist to evaluate image based and projection based concurrentrest/stress kinetic parameter estimation techniques. For example, MonteCarlo simulations and phantom acquisitions can be extended to generatedynamic PET data for concurrent rest-stress studies. Metrics (e.g., areaunder the ROC) are used to compare the performance of the presentimage-based and projection-based approaches to that of the traditionalstrategy consisting in first reconstructing the dynamic volume and thenfitting each pixel TAC independently.

EXAMPLE 1 Non-Steady State Modeling Approach

To evaluate and compare the methods proposed in this work, simulationstudies were performed based on the kinetic parameters derived fromhuman studies conducted with ¹⁸F-Flurpiridaz, formerly known as¹⁸F-BMS747158-02 (Lantheus Medical Imaging, North Billerica, Mass.). Asa pyridazinone analog, ¹⁸F-Flurpiridaz binds to the mitochondrialcomplex I with high affinity. The combination of high first passextraction and complex I binding ensure a high uptake in the myocardiumwith long residence time, due to the high mitochondrial density in themyocardial wall. Data from a rat model have shown that ¹⁸F-Flurpiridazhas an extraction fraction of greater than 90% over the physiologicalMBF range, which makes it an attractive perfusion tracer. Validation ofMBF measured with ¹⁸F-Flurpiridaz has been performed by Nekolla et al.in pigs by comparison to microsphere and ¹³N-ammonia flow measurements.Good agreement between the MBF from ¹⁸F-Flurpiridaz and microspheres hasbeen demonstrated to exist for a wide range of blood flow values.

As an example case, kinetic analysis was carried out based on¹⁸F-Flurpiridaz clinical data. ¹⁸F-Flurpiridaz data was used todetermine the local myocardial kinetics used in the simulations. Thesedata included three subjects who underwent rest and stress imaging. Inthese studies, rest and stress imaging sessions were separated by atleast 2 h. First, the rest and stress studies for each of the subjectswere analyzed separately. These data were fitted to the two-compartmenttissue model, using methods similar to a previous study (Nekolla, S. G.,et al., Circulation 119, 2333-2342, 2009). For each subject, theacquired stress images were first aligned to the images of the reststudy using the nonrigid image registration tool from SPM8 9 Ashburner,J., et al., Hum. Brain Mapp 7, 254-266, 1999). The LV and RV inputfunctions for each subject and each study were extracted withgeneralized factor analysis on dynamic series (GFADS) (El Fakhri, G., etal., J. Nucl. Med. 46, 1264-1271, 2005; El Fakhri, G., et al., Med.Phys. 33, 1016-1024, 2006). The LV input function was used toapproximate the plasma input function. No metabolite correction wasperformed. Because there is a 2-h gap between the rest and stressstudies, the residual myocardial activity from the first tracer ismostly tracer bound to MC1 and is nearly unaffected by the stressor. Therest and stress image volumes of the heart were segmented into 17standard segments. Time-activity curves were calculated for eachsegment. The residual activity from the rest study was estimated andsubtracted from the stress time-activity curve. Then, the kineticparameters were estimated for each segment under the resting state withits 25-min time-activity curve. k₃ was fixed as 0.026 (1/min) during thecurve fitting. For the time-activity curve of the stress study, onlydata within the first 7.5 min were used in the curve fitting. After theparameters were estimated for all 17 segments, the cardiac flow reserve(CFR=K_(1,s)/K_(1,r)) of each segment was calculated. For the leftanterior descending (LAD) territory, the segments with the two lowestCRF values were selected and the mean of each kinetic parameter wascalculated from the estimated parameters of those two segments. The samecalculation was repeated for right coronary artery (RCA) and leftcircumflex (LCX) as well. After two segments were picked for eachterritory, the kinetic parameters of the other 11 segments were averagedto compute the average kinetic parameters of the normal myocardium. Thecomputation of the kinetic parameters of the normal myocardium, LAD,LCX, and RCA territories was performed separately for each subject.Table 1 summarizes kinetic parameters K₁ and k₂ estimated from theclinical ¹⁸F-Flurpiridaz studies and used for Monte Carlo simulations.

TABLE 1 Rest Stress Subject Myocardium K₁ k₂ K₁ k₂ 1 Normal 0.68 0.081.96 0.07 LAD 0.56 0.07 1.04 0.05 RCA 0.74 0.08 1.26 0.08 LCX 0.58 0.080.99 0.05 2 Normal 0.74 0.11 2.29 0.12 LAD 0.78 0.11 1.60 0.07 RCA 0.680.08 1.43 0.11 LCX 0.94 0.13 2.10 0.10 3 Normal 0.60 0.05 2.09 0.23 LAD0.64 0.04 1.58 0.18 RCA 0.58 0.03 0.68 0.17 LCX 0.69 0.05 1.61 0.24

Due to the relatively short measurements obtained in human studies,longer duration ¹⁸F-Flurpiridaz measurements were made in a healthy4.1-kg cynomolgus monkey. The animal was imaged under resting state andgeneral anesthesia with 1%-2% isoflurane mixed with 100% oxygen. A100-min dynamic ¹⁸F-Flurpiridaz PET study, with 1.25 mCi injected dose,was acquired with a microPET P4 system from Concorde Microsystems, Inc.Images were reconstructed with filtered back-projection (FBP) into24×5-s, 6×30-s, 5×60-s, and 18×300-s frames. The LV and RV inputfunctions were extracted with GFADS. A region of interest was drawn overthe myocardium and used to calculate the time-activity curve which wasthen fitted to the standard four parameter, two-compartment model, withcorrections for blood-to-tissue spillover in Eq. (3). Compartment modelkinetic analysis tool (COMKAT) (Muzic Jr., R. F., et al., J. Nucl. Med.42, 636-645, 2001; Fang, Y. H., et al., J. Nucl. Med. 51, 77-84, 2010)under MATLAB R2009a (Mathworks, Natick Mass.) was used to perform thenonlinear least squares for parameter estimation with the frame durationused as the statistical weight. After the kinetic parameters wereestimated from the 100-min time-activity curve, the data were refitwhile progressively reducing the observation period. First, K₁, k₂,f₁,and f_(RV) were estimated as a function of study duration of thetime-activity curves, ranging from 1 to 10 min. Due to the shortmeasurement periods used, k₃ and k₄ were fixed to the values estimatedby the 100-min data. The parameter variance of each parameter wasapproximated by the covariance matrix from the nonlinear least squaresfitting. Then, the duration of the time-activity curve was fixed to 10min and estimation of K₁, k₂, f₁, and f_(RV) was repeated with k₃ fixedat values ranging from 0 to 0.1, incremented by 0.005. k₄ was fixed asthe value derived from fitting the 100-min data. Again, variance of eachestimated parameter was approximated by the covariance matrix.

To provide an initial evaluation of the proposed new single-sessionrest/stress measurement strategy and to compare with the conventionalmodel for estimating the MBF estimation, Monte Carlo techniques wereused to simulate realistic dynamic PET studies based on kineticparameters obtained from human studies with¹⁸F-Flurpiridaz. The kineticparameters were estimated as described above for kinetic analysis basedon ¹⁸F-Flurpiridaz clinical data. Furthermore, the proposed models wereevaluated for application to different vasodilator kinetics. Thegeneration of simulated data was performed as follows: First, the NCATtorso phantom (Segars, W., et al., IEEE Trans. Nucl. ScL 46, 503-506,2002) was used to generate static activity maps of the LV, RV, lung,liver, and the soft tissue. The heart activity maps were also generatedwith NCAT, with transmural defects in the LCX, RCA, and LAD territoryand the rest of the myocardium is treated as the normal tissue. Withthose activity maps, high counts were simulated for each region withSIMSET (Harrison, R., et al., J. Nucl. Med. 34, 60, 1993), using avalidated procedure for simulating the Philips Gemini System (Guerin,B., et al., IEEE Trans. Nucl. Sci. 55, 942-952, 2008). The simulatedlist-mode data were Fourier-rebinned into two-dimensional sinograms as256×256 matrices over 87 slices. The simulated noise-free sinogram ofeach tissue type were then combined into a dynamic study by firstscaling each sinogram with the corresponding time-activity curve andthen summing those to form a simulated, noise-free sinogram for adynamic PET study. The simulated study duration is 20 min, with theframing scheme of 6×5-s, 3×30-s, 5×60-s, 3×120-s, 6×5-s, 1×30-s, 2×60-s,2×120-s frames. Then, Poisson deviates were added to the noise-freesinogram to form sinogram data with noise levels similar to thoseobserved in clinical studies.

Dynamic PET studies were simulated with two different ¹⁸F-Flurpiridazinjection doses and two different vasodilators: adenosine anddipyridamole. 2 mCi was used for the ¹⁸F-Flurpiridaz dose for the firstinjection at time zero. The second injected dose was either 2 mCi(single-dose) or 4 mCi (double-dose) at 13 min after the firstinjection. K₁(t) and k₂(t) are both modeled as piecewise linearfunctions as Eqs. (4,5,6), respectively. For dipyridamole, the followingtiming information was used to simulate the data: T_(S)=6.5, T_(P)=13,T_(E)=23, and T_(B)=29 (minutes). The choice of T_(P) and T_(E) wasbased on the dipyridamole kinetics reported previously. For adenosine,the following timing information was used to simulate the data:T_(S)=11.5, T_(P)=13, T_(E)=17.5, and T_(B)=20. T_(P) and T_(B) werechosen based on the kinetics previously reported under a 6-min adenosineinfusion (T_(E)=T_(S)+6).

For each combination of vasodilator and stress tracer dose, twenty noiserealizations were simulated for each of the three simulated subjects.When simulating data for each subject, the kinetic parameters used tosimulate the time-activity curve of a specific myocardial region weretaken from the corresponding region of this specific subject. Forexample, the time-activity curve simulated the normal myocardium for thesecond subject is calculated with the parameters K_(1,r)=0.738,k_(2,r)=0.108, K_(1,s)=2.292, and k_(2,s)=0.118. In addition, for eachnoise realization, both the sinogram of a stress-only study and asingle-scan rest/stress study were simulated. The stress-only study waslater used as the reference for comparison.

After the sinograms were simulated with the previous steps, images werereconstructed with FBP (Hamming, cutoff=0.7) for 50 slices that coverthe whole heart. In the normal myocardium and the three territories, a˜2mL ROI was used to calculate the time-activity curves. For each type oftissue and each combination of subject/vasodilator/stress dose, thereare two time-activity curves per noise realization: the stress-onlycurve and rest/stress curve.

Parameter estimation was performed following extraction of thetime-activity curves from the reconstructed images. Time-activity curveswere fitted to the kinetic models previously described. First, thestress-only time-activity curve was used to estimate K_(1,s) andk_(2,2). The reference peak MBF was set to be equal to K_(1,2) estimatedfrom the stress-only time-activity curves. For the time-activity curvesof the rest/stress study, the reference peak MBF was fitted to the MGH1model in two steps: First, the portion of the time-activity curve beforethe vasodilator infusion was fitted to MGH1 for estimating K_(1,r),k_(2,r), f₁, and f_(RV). After that, K_(1,r), k_(2,r), f₁, and f_(RV)were fixed to values estimated from the previous step while K_(1,2),k_(2,s), T_(P), T_(E), and T_(B) were estimated with Eqs. (4,5). Theinitial guess and bounds for parameter estimation are summarized inTable 2. Note that T_(B) is not estimated for dipyridamole because TE isgreater than 20 min, which is the total study duration.

TABLE 2 Dipyridamole Adenosine K_(1,r) k_(2,r) K_(1,s) k_(2,s) f₁ f_(RV)T_(P,S) T_(E,S) T_(P,S) T_(E,S) T_(B,S) Initial guess 0.5 0.1 1 0.1 0.050.05 11.5 23.5 12 17.6 19 Lower bound 0 0 0 0 0 0 8.5 21.5 11.5 17.518.5 Upper bound 5 0.3 10 0.3 0.25 0.25 14.5 26.5 13.5 18.5 21

For the MGH2 model, the same procedure was used for fitting thetime-activity curves. The parameters K_(1,r), k_(2,r), f₁, and f_(RV)were first estimated from the time-activity curve before vasodilatorinfusion and then fixed when estimating K_(1,s), k_(2,s). All theparameter estimation procedures were performed with COMKAT using thenonlinear weighted least squares with weights equal to the framedurations.

With respect to statistical analysis, for each combination ofregion/subject/vasodilator/stress dose, the sample mean and standarddeviation of the estimated K_(1,s) were computed over the twenty noiserealizations. The K_(1,s) estimated from the stress-only study was usedas the reference. The mean and SD of K_(1,s) from different models wereplotted as bar graphs to compare the estimated results between thosefrom the Standard method and those estimated from MGH1 and MGH2. Foreach vasodilator/stress dose combination, the average bias percentagewas calculated by averaging the bias percentage either over the subjectsor over the tissue types (four tissue types, three subjects). Toevaluate the precision of MBF estimation for each model, the SDpercentage was computed as follows: the SD was first calculated from thetwenty noise realizations for each tissue type/subject/dose, and thendivided by the mean of estimated MBF to form the relative SD percentage.Following that, the average SD percentage for each method was calculatedby averaging over regions of the subjects. The average bias and SDpercentages were plotted as bar graphs.

In conclusion, the present system and methods have shown with simulationstudies that MBF can be accurately and precisely estimated from asingle-scan rest/stress study for a long half-lived tracer such as¹⁸F-Flurpiridaz. The rest/stress model requires a single input functionfor rest and stress, eliminating the need for extrapolation orsubtraction. Accounting for the time-dependence of the kineticparameters, the proposed models (MGH1 and MGH2) both achieve goodaccuracy and precision for different vasodilators and a range ofdifferent MBF states.

EXAMPLE 2 Advanced Modeling Approach

Aspects of the present system and methods relate to validation ofsequential and concurrent rest-stress cardiac PET in acute ischemicporcine model. One approach involves validation of the measurement ofmyocardial blood flow in a sequential rest-stress study. The accuracyand precision with which MBF (in mL/g/min) can be measured is, in oneexample, evaluated in pigs using sequential rest-stress ¹⁸F-Flurpiridazcardiac PET. Estimates of MBF obtained with the present approaches indynamic ¹⁸F-Flurpiridaz are compared to flows measured with microspheresover a large range of flow values.

With respect to an example surgical protocol, eight domestic swine ofeither sex can be used. Endotracheal intubation and mechanicalventilation can be performed under general anesthesia. All pigs can bepre-medicated with the intravenous administration of thiamylal (22mg/kg) prior to intubation and surgery. Anesthesia will be maintainedwith Halothane (0.5-1.5%), and a mixture of nitrous oxide (60%) andoxygen (40%). Central hemodynamics (e.g., arterial pH, pCO₂, pO₂) andECG can be surveyed and maintained within the physiological range. Athoracotomy can be performed in the left fifth intercostal space toinsert a catheter into the left atrium for injection of themicrospheres. To induce the acute stenosis, the right internal carotidartery (RICA) in the neck can be isolated and surgical ties positioned 1cm apart to permit control of bleeding when the vessel is incised. TheRICA is used to insert a 7FR Amplatz catheter to engage the leftanterior descending (LAD) coronary artery. A 3FR angioplasty balloon canbe passed through the Amplatz catheter. The balloon can be guided underfluoroscopic control to the distal third of the LAD. The ballooncatheter can be connected to a pressure gauge to monitor LAD pressurepre- and post-balloon inflation. During the inflation, a substantialpressure gradient can be create between the aorta and the coronaryartery distal (−40 to 60 mmHg).

In one example, the balloon will occlude the LAD for 40 minutes or untilthe end of corresponding PET study.

A sequential rest-stress protocol, in one aspect, can involve two PETstudies on separate days performed in each pig. On Day 1, the animalundergoes a dynamic PET study of ¹⁸F-Flurpiridaz for 60 minutes underthe resting state without stenosis. Radioactive microspheres areinjected via a catheter into left atrium, to provide a reference flowmeasurement. On Day 2, the pig is induced with a stenosis as describedin the previous section. Once hemodynamic variables have stabilized, theanimal is infused with dipyridamole intravenously for six minutes. Twodifferent doses of dipyridamole (0.1 mg/kg/min and 0.2 mg/kg/min) areused to create different peak MBF with four pigs in each group. Bloodpressure and heart rate are used to determine whether the peak hyperemiahas been reached. Once the peak hyperemia has been reached, a dynamicPET study of ¹⁸F-Flurpiridaz is performed for 30 minutes accompanied bythe injection of microspheres with a different radioisotope than thoseused in Day 1. During the PET study, 10 arterial blood samples are takenfor measurement of the reference input function. At the end of the PETstudy the pigs are sacrificed and the excised hearts trimmed of theatria and right ventricle. The remaining left ventricle are cut intoeight slices of equal thickness, and each of the myocardial slices arecut into eight radial sections, resulting in 64 myocardial segments ofthe heart. Tissue samples are counted in a well counter for measurementof microsphere flow after correction for background, decay andspillover.

Eight pigs can be used to measure sequential rest and stress myocardialblood flow. 4 pigs receive the 0.1 mg/kg and 4 receive the 0.2 mg/kgdipyridamole stress, resulting in 512 values of MBF in 8 pigs atbaseline, 256 MBF values in 4 ischemic pigs at stress induced by 0.1mg/kg of dipyridamole and 256 MBF values in 4 ischemic pigs at stressinduced by 0.2 mg/kg. It can be determined if dynamic ¹⁸F-FlurpiridazPET MBF measures in the 3 coronary territories can be strongly andpositively (r>0.8) correlated with measures obtained using microspheresby correlation analysis.

In one example, the endpoints for analysis are PET measurements of localMBF obtained from the first 6, 10 and 60 minutes of PET scan. The 6 and10 minute endpoints can use a simplified kinetic

For each of the 64 myocardial segments, the MBF is measured from themicrosphere activity as reference and read-out from the PET flow imagesby ROI analysis. The PET flow values for each method are entered into anANOVA table classified by individual pig, segment, and measurementnumber.

The classification variables for the ANOVA can be segment and method;microspheres flow can be entered as a linear regressor. In some aspects,the data can be pooled into a single regression, independent of segmentlabel. The mean values of MBF and standard error of the mean can becalculated for all myocardial blood flows estimated using themicrospheres methodology as well as by dynamic ¹⁸F-Flurpiridaz. Resultsare displayed using the traditional polar map.

A further aspect of the present system and methods includes validationof the measurement of myocardial blood flow in a concurrent rest-stressstudy. After the validation of the measured MBF in sequentialrest-stress ¹⁸F-Flurpiridaz cardiac PET studies, the measured MBF in aconcurrent rest-stress study can be validated in pig models. The MBFmeasured by microsphere can be used as reference.

In another example of a surgical protocol, eight domestic swine ofeither sex are used. The same surgical procedure as described above canbe used to incubate and anesthetize the animal. Acute myocardialischemia can be induced. A catheter can be inserted into the left atriumfor microsphere injection.

In another example of an experimental protocol, two injections of the¹⁸F-Flurpiridaz are performed within the same imaging session for eachpig. After the stenosis is induced and the hemodynamics of the animalbecome stable, the first ¹⁸F-Flurpiridaz dose (2 mCi) is injected whenthe animal is under the resting state. Microspheres are injected throughthe left atrial catheter at the time of peak stress. Six minutes afterthe first tracer injection, the pig is stressed with intravenousinfusion of dipyridamole for a period of five minutes. Four pigs canundergo a 0.1 mg/kg/min infusion and the other four can undergo a 0.2mg/kg/min infusion.

Hemodynamics, blood pressure and heart rate are continuously recordedand used to determine whether the peak hyperemia has been reached. Fiveminutes after the dipyridamole infusion, the second ¹⁸F-Flurpiridaz dose(2 mCi) is injected. Emission data is acquired for duration of 30minutes including both the rest and stress studies accompanied byarterial blood sampling for measuring the reference input function. Atthe end of the PET study the pigs are sacrificed and myocardial samplesare prepared and processed.

For each of the 64 myocardial segments, the MBF can be measured from themicrosphere activity as reference and estimated from the PET images. TheMBF can be estimated from concurrent rest-stress ¹⁸F-Flurpiridazstudies. Flow data is analyzed with a linear statistical model in whichmicrosphere flow is entered as a linear regressor and segment is aclassification variable. In one aspect, PET flow at rest and stress islinearly related to microsphere flow. The mean values of MBF andstandard error of the mean are calculated for all MBFs estimated usingthe microspheres methodology as well as by dynamic ¹⁸F-Flurpiridaz.Results are displayed using polar maps.

In aspect of the present system and methods includes realistic MonteCarlo simulation of PET imaging. A realistic simulation of randomcoincidences, pixelated block detectors, light sharing among crystalelements and dead-time in 2D and 3D positron emission tomography (PET)imaging based on the SimSET Monte Carlo simulation software package hasbeen developed and validated (Lewellen, T. K., et al., Monte Carlosimulations in nuclear medicine, 1998, 10P Publishing: Philadelphia. p.77-92). The present simulation was validated by comparison to a MonteCarlo transport code widely used for PET modeling, GATE, and tomeasurements made on two GE Discovery STE PET scanners (Guerin, B. etal., IEEE Nucl. Sci., 2008. 55(3): p. 942-952).

Another aspect of the present system and methods includes Quantitationof myocardial blood flow using generalized factor and compartmentanalyses (GFADS). Generalized factor analysis of dynamic sequences(GFADS) methods and software were developed specifically for the case ofabsolute quantitation of ¹⁸F-Flurpiridaz MBF as shown in FIGS. 10A-10B.The factor and compartment analyses are incorporated in a user friendlysoftware that allows automated estimation of regional MBF. The intra-and inter-observer variability of MBF estimation was determined using⁸²Rb PET as well as the reproducibility of our GFADS +compartmentanalyses methodology (El Fakhri, G., et al., J Nucl Med, 2009. 50: p.1062-1071; El Fakhri, G., et al., J Nucl Med, 2005. 46: p. 1264-1271;Anagnostopoulos, C., et al., Eur J Nucl Med Mol Imag, 2008. 35: p.1593-1601; Curillova, Z., et al., Eur J Nucl Med Mol Imag, 2009. 35).

Yet another aspect of the present system and methods includes evaluatingthe effect of input functions estimation with GFADS and ROIs on MBFcomputation accuracy. The impact was assessed of the estimation of the¹⁸F-Flurpiridaz input function, using GFADS versus manual region ofinterest (ROI), on the accuracy of myocardial blood flow quantitation(MBF) in realistic cardiac dynamic Monte Carlo simulations as well ascynomolgus monkey studies FIGS. 11A-11B show the reference leftventricular input function that was modeled in realistic dynamic MonteCarlo simulations as well as the estimated one with GFADS and using ROI.Compared to the “true” input functions used for simulation, the initialpeaks of GFADS input functions are closer to the true input than theROI-based input functions (due to less partial volume effect) and thetail of GFADS input aligns with the true input function very well (dueto less spillover). The bias in MBF estimation, associated with theestimated LV and RV input functions, was 3.4±5.1% with GFADS and18.2±7.7% with ROI. Similar conclusions are drawn from the monkeyexperiment where the GFADS LV factor was in much greater agreement withblood samples than the ROI TAC. The results showed that the MBFestimated with GFADS is more accurate than those obtained with ROI-basedinputs.

With respect to ¹⁸F-flurpiridaz patient studies, MBF was estimated withkinetic modeling analysis in a group of subjects in phase II clinicaltrial of ¹⁸F-Flurpiridaz. Rest and stress PET studies were performedsequentially on 10 healthy subjects and 6 CAD patients as shown in FIGS.12A-12B. The estimated MBF data for rest-stress and healthy/ischemicmyocardium are summarized in Table 3. Specifically, table 3 shows MBF(mL/g/min) estimated in clinical ¹⁸F-Flurpiridaz studies. Unlikeischemic MBF, normal MBF increased significantly during stress.

During the rest study, there was no significant difference in MBF(t-test, α=0.05) between the healthy and ischemic myocardium. However,during pharmacological stress, MBF increased more than 3× in healthymyocardium but remained approximately the same in ischemic regions. TheMBF in ischemic myocardium showed no significant difference between restand stress phases. The MBF measured with kinetic modeling for¹⁸F-Flurpiridaz are consistent with the previously reported MBF measuredwith other tracers. The preliminary kinetic analysis of the¹⁸F-Flurpiridaz shows that it has a high potential for absolutequantitation of MBF in clinical cardiac PET studies.

TABLE 3 Healthy Normal Myocardium Ischemic Subjects in PatientsMyocardium Rest 0.67 ± 0.01 0.73 ± 0.04 0.86 ± 0.06 Stress 2.84 ± 0.061.78 ± 0.07 0.73 ± 0.07

Still another aspect of the present system and methods includes¹⁸F-Flurpiridaz myocardial blood flow estimation using dynamic PET innon-human primates. To evaluate the parameter precision when estimatingthe MBF, a long dynamic study was conducted on a 4.1-kg cynomolgusmonkey and the MBF precision was examined with different durations ofdata being used for kinetic modeling. A dynamic ¹⁸F-Flurpiridaz imagingstudy was performed on a microPET P4. The animal was imaged under theresting state for 100 minutes with an injected dose of 1.25 mCi. FIG. 13shows the summed images of the myocardium in the short and long axis.The LV (FIGS. 11A-11B) and RV input function were extracted with GFADS.Time-activity curve from a region of interest drawn over the myocardiumwas fitted to the 2-compartment model. The estimated parameters are:K₁=0.51 (SD=1.5%), k₂=0.075 (SD =4.7%), k₃ =0.026 (SD=10.2%) and k₄=0.016 (SD=10.4%). FIG. 14A shows the fitted time-activity curve. Toevaluate the precision of K₁ and k₂, different study durations wereused, ranging from one minute to 10 minutes to estimate K₁ and k₂ (withk₃ and k₄ fixed) as plotted for K₁ in FIG. 14B. With 10 minutes of data,both K₁ and k₂ were found to achieve a high precision (SD<10%),indicating that a concurrent rest/stress study of 30 minutes issufficient to provide a reliable estimate of the MBF and the CFR.Furthermore, truncating the study duration to 3-10 min resulted in lowbias in K₁ estimates (less than 4%) compared to using whole 100 minutesas shown in FIG. 14B. In addition, since the concentration curve isinsensitive to k₃ and k₄ in the early frames of study, using nominal k₃values between 0 and 0.1, was demonstrated to not bias the K₁ estimatesmore than 5% as in FIG. 14C.

A further aspect of the present system and methods includes concurrentrest-stress dynamic cardiac PET with Monte Carlo simulations. Theaccuracy and precision of the MBF estimated from the proposed concurrentrest-stress study was assessed with Monte Carlo simulations. Thesinograms of different organs and myocardial defects were simulated withmethods described herein. A dynamic PET study was simulated by the inputfunctions and kinetic parameters from patient studies. During the reststudy, MBF was simulated as 0.54 (mL/g/min) for all myocardial tissues.During stress, the simulated MBF are 1.1 (mL/g/min) for normalmyocardium, 0.76 (mL/g/min) for ischemic myocardium and 0.54 (mL/g/min)for infarcted myocardium. The two different degrees of ischemic severityin all three coronary territories (LCX, LAD and RCA) was also simulated.The study duration and injection times were simulated according to theproposed scheme. Two injection doses were simulated: double dose forstress (2mCi for rest and 4 mCi for stress) and single stress dose (2mCi for rest and 2mCi for stress.) The results of estimated MBF wereplotted in FIGS. 15A-15B. With the present model, MBF was estimatedreliably with a bias of less than 5% and the SD of less than 10% in bothstress doses and in all the coronary territories.

No significant difference was found in the MBF estimated with our modelas compared to the MBF estimated from sequential rest-stress studies. Asthe conventional model does not accurately account for the effect ofvasodilator in the model, it will overestimate the MBF during stresswith 10-15% bias in the double-dose stress study as shown in FIG. 15A,and 20-30% bias in the single-dose stress study as shown in FIG. 15B.The data show that the present system and methods yield accurate kineticparameter estimates for concurrent rest-stress that are notstatistically different (NS) from sequential rest-stress.

Concurrent rest-stress cardiac PET with patient data simulations werealso performed. The accuracy and precision of the MBF estimated from theproposed concurrent rest-stress study was assessed with simulationsusing clinical data. With the sequential rest-stress patient data, inwhich the rest and stress studies were performed in separate sessions,TACs were simulated under a concurrent rest-stress scenario. First, thekinetic parameters of k₁˜k₄ were estimated from separate rest and stressstudies. Following that, the original rest-only TAC was extended toinclude the effect of pharmacological stress using the proposed modeland then added with Gaussian noise. This simulated TAC was summed withthe stress-only TAC to form a TAC that mimics a TAC from a patientconcurrent rest-stress study. FIG. 16 shows the plot of the regressionanalysis between the MBF estimated from sequential and concurrentrest-stress studies. A high correlation of R=0.99 and a slope of 0.98was calculated. Clinical data show that the present system and methodsallow accurate estimation of MBF in concurrent rest-stress.

Thus, in the foregoing, new systems and methods have been described. Inone aspect, the approach is two-fold. In a first aspect, advances weremade over prior methods by including more rigorous non-steady-statemeasurements. Specifically, two new dual-injection kinetic models havebeen developed that represent the response to pharmacological stress asa transitional or transient increase of MBF. The theory of the method isdescribed and the feasibility of clinical measurement is evaluated byrealistic simulation with an ¹⁸F flow tracer. In one aspect,¹⁸F-Flurpiridaz, a new myocardial imaging agent now in Phase III trialsis considered as the ¹⁸F flow tracer.

In a second aspect, the present system and methods address the need foradequate SNR in parametric images and a compelling application, one thatcan achieve wide clinical utilization. Automated indirect as well asdirect parametric reconstruction can provide dramatic improvement of theSNR of parametric images, between 2× and 6× conventional methods. Thisis a general technique that has important applications in many organsystems beyond the heart.

With regard to the compelling application, there are several myocardialimaging agents that are in clinical trial. One example of an imagingagent is ¹⁸F-Flurpiridaz, which is now under Phase III study.¹⁸F-Flurpiridaz has nearly complete first-pass extraction in thecoronary circulation, which can be an important factor in selecting amyocardial flow tracer. For the first few minutes after injection, thedistribution of ¹⁸F-Flurpiridaz depends almost entirely on coronaryblood flow; later, the distribution depends on both flow and binding tomitochondrial complex I. Methods previously developed for flowmeasurement with ⁸²Rb, and ¹³N are informative but not optimal given thekinetics and 110 minute half-life of ¹⁸F-Flurpiridaz studies.

Current analytic methods assume that a pharmacological stress challengeinduces a new steady state with respect to blood flow, one thatpersists, unchanged, throughout the imaging session. But in realitypharmacological stress increases flow which rises to a plateau and thendeclines, as opposed to exhibiting steady-state behavior. Accordingly,the flow should be considered to be time dependent. In a firstembodiment, the present system and method have shown that the assumptionof time dependence significantly reduces bias in the measurement. In oneaspect, a more realistic kinetic measurement strategy allows for restand stress imaging to be conducted in a single imaging session lastingabout 30 minutes. The analysis derives a single rest-stress inputfunction (without operator intervention) using generalized factoranalysis (GFADS). Rest and stress blood flow are estimated jointly froma single rest-stress data set. In one example, a single rest-stress¹⁸F-based imaging session can be completed in about 30 minutes, yieldingabsolute rest and stress blood estimates with high accuracy and lownoise.

In certain embodiments, the present system provides for completion of arest-stress imaging session in a single visit requiring only 30 minutesimaging time. In other embodiments, all of the proposed techniques areincorporated into a software system that can run unattended, so thatclinical or research staff may concentrate on throughput and patientinteractions.

In still other embodiments, the present system provides a directparametric reconstruction capable of quantitative parametric imagingfrom listmode data. A computationally feasible direct parametricreconstruction (DPR) approach acting on listmode projections andincorporating quantitative corrections for attenuation, detectorsensitivity, scatter and random coincidences can yield parametric mapsthat are significantly less noisy than those obtained with thetraditional indirect approach (image reconstruction +pixel-by-pixel timeactivity curve (TAC) fitting). This strategy is applicable to dynamiccardiac imaging with ¹⁸F-based tracers such as ¹⁸F-Flurpiridaz. However,similar gains in other organs, such as the brain are also possible. Thedrastic reduction in noise with this technique is due to the use oftemporal regularization in the DPR process and the more correct modelingof the noise statistics in the projection than in the image domain.High-quality parametric maps of the myocardium can be obtained whilesignificantly lowering the injected dose (and therefore rest-stressspillover and radiation dose to the patient). It has been demonstratedthat DPR allows for injection of a low activity dose in the rest part ofthe concurrent rest-stress protocol, making it easier to estimate restand stress myocardial parametric maps in a single imaging session. Inanother aspect, the noise reduction benefit of DPR is extendable toconcurrent rest-stress flow mapping. One advance is the implementationof DPR to directly estimate kinetic parameters from listmode data whilecorrecting for the effects of scatter, randoms, detector sensitivity,and the like.

The present methods can be evaluated in an acute ischemic porcine model.The experiments are sequential. This approach allows for thedemonstration of the in vivo validity of the methodological developmentsand provides the information needed to undertake human studies.

The present invention has been described in terms of one or morepreferred embodiments, and it should be appreciated that manyequivalents, alternatives, variations, and modifications, aside fromthose expressly stated, are possible and within the scope of theinvention.

Each reference identified in the present application is hereinincorporated by reference in its entirety.

While present inventive concepts have been described with reference toparticular embodiments, those of ordinary skill in the art willappreciate that various substitutions and/or other alterations may bemade to the embodiments without departing from the spirit of presentinventive concepts. Accordingly, the foregoing description is meant tobe exemplary, and does not limit the scope of present inventiveconcepts.

A number of examples have been described herein. Nevertheless, it shouldbe understood that various modifications may be made. For example,suitable results may be achieved if the described techniques areperformed in a different order and/or if components in a describedsystem, architecture, device, or circuit are combined in a differentmanner and/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe present inventive concepts.

1. A system for performing a single-scan rest-stress cardiacmeasurement, the system comprising: a positron emission tomography (PET)imaging system configured to acquire PET 5 imaging data from a subjecthaving received a first PET radiotracer and a second PET radiotracer; aprocessor having non-transient computer readable media programmed withinstructions to cause the processor to: generate PET images of thesubject administered with the first and second radiotracer from the PETimaging data, process the PET images with a non-steady-state,multi-compartment parametric model, and generate a report indicating anoutput of the non-steady-state, multi-compartment parametric modelindicating a measure of myocardial blood flow for both a rest state anda stress state of the subject.
 2. The system of claim 1, wherein PETimaging system is configured to acquire the PET imaging data during asingle scan session of less than an hour in duration.
 3. The system ofclaim 1, wherein at least one of the first and second PET radiotracershas a half-life of at least 60 minutes.
 4. The system of claim 3,wherein at least one of the first and second PET radiotracers is an¹⁸F-based tracer.
 5. The system of claim 4, wherein the ¹⁸F-based traceris ¹⁸F-Flurpiridaz.
 6. A system for performing a single-scan rest-stresscardiac measurement, the system comprising: a positron emissiontomography (PET) imaging system; a source of a first PET radiotracer foradministration to a subject; a source of a second PET radiotracer foradministration to a subject; and a processor having non-transientcomputer readable media programmed with instructions to cause theprocessor to obtain PET listmode data from the subject administered withthe radiotracer, the instructions further causing the processor toprocess the PET listmode data by way of a direct parametricreconstruction (DPR) of kinetic parameters from the listmode data,wherein an output of the DPR is a measure of myocardial blood flow forboth a rest state and a stress state of the subject.
 7. A system forestimating kinetic parameters of blood flow of a subject, the systemcomprising: a processor having non-transient computer readable mediaprogrammed with instructions to cause the processor to: obtain PET imagedata from the subject, the PET image data acquired and timed with atleast one real-time measurement of a physiological variance of thesubject; access a model for estimating kinetic parameters within of akinetic region of interest in the PET image data from the non-transientcomputer readable media, the kinetic parameters based on an expressionof the radiotracer within the PET image data, generate a report of atleast one kinetic parameter of blood flow based on the kineticparameters estimated from the PET image data.
 8. The system of claim 7wherein the kinetic parameters are further based on a real-timemeasurement of physiological variance of the subject.
 9. The system ofclaim 7 wherein the model is capable of executing a direct parametricreconstruction (DPR) of the kinetic region of interest.
 10. The systemof claim 7 wherein the at least one kinetic parameter of blood flowcomprises myocardial blood flow during both rest and stress states ofthe subject .
 11. The system of claim 10 wherein the radiotracer isadministered prior to the rest state and again prior to the stressstates of the subject.
 12. The system of claim 10 wherein the stressstate is induced pharmacologically.
 13. The system of claim 7 the PETimage data is acquired within a total time period of about 30 minutes orless.
 14. The system of claim 8 wherein the at least one real-timemeasurement of a physiological variance of the subject comprises acomputation of ventricle input functions.
 15. The system of claim 14wherein the computation of ventricle input functions comprisesgeneralized factor analysis and dynamic orthogonal grouping.
 16. Thesystem of claim 7 wherein the radiotracer permits multi-compartmentkinetic modeling and has a high first-pass extraction rate with few orslow biochemical reactions.
 17. The system of claim 16 wherein the fewor slow biochemical reactions result in long tissue residence periods.18. The system of claim 16 wherein the multi-compartment kineticmodeling is extended to include time varying parameters reflecting theboth rest and stress conditions.
 19. The system of claim 16 wherein theradiotracer comprises ¹⁸F-Flurpiridaz.
 20. The system of claim 9 whereinthe DPR comprises incorporation of time-of-flight information pertainingto the PET images.